Original: SwipeRing: Gesture Typing on Smartwatches Using aCircular Segmented Q WERTY 
Revised: SwipeRing: Gesture Typing on Smartwatches Using a Segmented Q WERTY Around the Bezel

Original: [Figure]
Revised: [Figure]

Original: A BSTRACT
Revised: A BSTRACT

Original: Most text entry techniques for smartwatches require repeated taps to enter one word, occupy most of the screen, or use layouts that are difﬁcult to learn.
Revised: Most text entry techniques for smartwatches require repeated taps to enter one word, occupy most of the screen or use layouts that are difﬁcult to learn.

Original: Users are usually reluctant to use these techniques since the skills acquired in learning cannot be transferred to other devices.
Revised: Users are usually reluctant to use these techniques since the skills acquired in learning cannot be transferred to other devices.

Original: SwipeRing is a novel keyboard that arranges the Q WERTY layout around the bezel of a smartwatch divided into seven zones to enable gesture typing.
Revised: SwipeRing is a novel keyboard that arranges the Q WERTY layout around the bezel of a smartwatch divided into seven zones to enable gesture typing.

Original: These zones are optimized for usability and to maintain similarities between the gestures drawn on a smartwatch and a virtual Q WERTY to facilitate skill transfer.
Revised: These zones are optimized for usability and to maintain similarities between the gestures drawn on a smartwatch and a virtual Q WERTY to facilitate skill transfer.

Original: Its circular layout keeps most of the screen available.
Revised: Its ring-shaped layout keeps most of the screen available.

Original: We compared SwipeRing with C-Q WERTY that uses a similar layout but does not divide the keys into zones or optimize for skill transfer and target selection.
Revised: We compared SwipeRing with C-Q WERTY that uses a similar layout but does not divide the keys into zones or optimize for skill transfer and target selection.

Original: In a study, SwipeRing yielded a 33% faster entry speed (16.67 WPM) and a 56% lower error rate than C-Q WERTY 
Revised: In a study, SwipeRing yielded a 33% faster entry speed (16.67 WPM) and a 56% lower error rate than C-Q WERTY .

Original: 1 I NTRODUCTION
Revised: 1 I NTRODUCTION

Original: Smartwatches are becoming increasingly popular among mobile users [31].
Revised: Smartwatches are becoming increasingly popular among mobile users [32].

Original: However, the absence of an efﬁcient text entry technique for these devices limits smartwatch interaction to mostly controlling applications running on smartphones (e.g., pausing a song on a media player or rejecting a phone call), checking notiﬁcations on incoming text messages and social media posts, and using them as ﬁtness trackers to record daily physical activity.
Revised: However, the absence of an efﬁcient text entry technique for these devices limits smartwatch interaction to mostly controlling applications running on smartphones (e.g., pausing a song on a media player or rejecting a phone call), checking notiﬁcations on incoming text messages and social media posts, and using them as ﬁtness trackers to record daily physical activity.

Original: Text entry on smartwatches is difﬁcult due to several reasons.
Revised: Text entry on smartwatches is difﬁcult due to several reasons.

Original: First, the smaller key sizes of miniature keyboards make it difﬁcult to tap on the target keys (the “fat-ﬁnger problem” [52]), resulting in frequent input errors even when augmented with a predictive system.
Revised: First, the smaller key sizes of miniature keyboards make it difﬁcult to tap on the target keys (the “fat-ﬁnger problem” [55]), resulting in frequent input errors even when augmented with a predictive system.

Original: Correcting these errors is also difﬁcult, and often results in additional errors.
Revised: Correcting these errors is also difﬁcult, and often results in additional errors.

Original: To address this, many existing keyboards use a multi-action approach to text entry, where the user performs multiple actions to enter one letter (e.g., multiple taps).
Revised: To address this, many existing keyboards use a multi-action approach to text entry, where the user performs multiple actions to enter one letter (e.g., multiple taps [15], chords [41]).

Original: This increases not only learning time but also physical and mental demands.
Revised: This increases not only learning time but also physical and mental demands.

Original: Besides, most existing keyboards cover much of the smartwatch touchscreen (50–85%), reducing the real estate available to view or interact with the elements in the background.
Revised: Besides, most existing keyboards cover much of the smartwatch touchscreen (50–85%), reducing the real estate available to view or interact with the elements in the background.

Original: Many keyboards for smartwatches that use novel layouts [1] do not facilitate skill transfer.
Revised: Many keyboards for smartwatches that use novel layouts [1] do not facilitate skill transfer.

Original: That is, the skills acquired in learning new keyboards are usually not usable on other devices.
Revised: That is, the skills acquired in learning new keyboards are usually not usable on other devices.

Original: This discourages users from learning a new technique.
Revised: This discourages users from learning a new technique.

Original: Further, most of these keyboards were designed for square watch-faces, thus do not always work on round screens.
Revised: Further, most of these keyboards were designed for square watch-faces, thus do not always work on round screens.

Original: Finally, some techniques rely on external hardware, which is impractical for wearable devices.
Revised: Finally, some techniques rely on external hardware, which is impractical for wearable devices.

Original: To address these issues, we present SwipeRing, a circular key- board that sits around the smartwatch bezel to enable gesture typing with the support of a statistical decoder.
Revised: To address these issues, we present SwipeRing, a ring-shaped keyboard that sits around the smartwatch bezel to enable gesture typing with the support of a statistical decoder.

Original: Its circular layout keeps most of the touchscreen available to view additional information and perform other tasks.
Revised: Its ring-shaped layout keeps most of the touchscreen available to view additional information and perform other tasks.

Original: It uses a Q WERTY -like layout divided into seven zones that are optimized to provide comfortable areas to initiate and release gestures, and to maintain similarities between the gestures drawn on a virtual Q WERTY and SwipeRing to facilitate skill transfer.
Revised: It uses a Q WERTY -like layout divided into seven zones that are optimized to provide comfortable areas to initiate and release gestures, and to maintain similarities between the gestures drawn on a virtual Q WERTY and SwipeRing to facilitate skill transfer.

Original: The remainder of the paper is organized as follows.
Revised: The remainder of the paper is organized as follows.

Original: First, we discuss the motivation for the work, followed by a review of the literature in the area.
Revised: First, we discuss the motivation for the work, followed by a review of the literature in the area.

Original: We then introduce the new keyboard and discuss its rigorous optimization process.
Revised: We then introduce the new keyboard and discuss its rigorous optimization process.

Original: We present the results of a user study that compared the performance of the proposed keyboard with the C-Q WERTY keyboard that uses a similar layout but does not divide the keys into zones or optimize for skill transfer and target selection.
Revised: We present the results of a user study that compared the performance of the proposed keyboard with the C-Q WERTY keyboard that uses a similar layout but does not divide the keys into zones or optimize for skill transfer and target selection.

Original: Finally, we conclude the paper with potential future extensions of the work.
Revised: Finally, we conclude the paper with potential future extensions of the work.

Original: 2 M OTIVATION
Revised: 2 M OTIVATION

Original: The design of SwipeRing is motivated by the following considera- tions.
Revised: The design of SwipeRing is motivated by the following considera- tions.

Original: 2.1 Free-up Touchscreen Real-Estate
Revised: 2.1 Free-up Touchscreen Real-Estate

Original: On a 30.5 mm circular watch, a standard Q WERTY layout without and with the suggestion bar occupy about 66% (480 mm 2 ) and 85% (621 mm 2 ) of the screen, respectively (Fig. 2).
Revised: On a 30.5 mm circular watch, a standard Q WERTY layout without and with the suggestion bar occupy about 66% (480 mm 2 ) and 85% (621 mm 2 ) of the screen, respectively (Fig. 2).

Original: On the same device, our technique, SwipeRing, occupies only about 36% (254.34 mm 2 ) of the screen, almost half the Q WERTY layout.
Revised: On the same device, our technique, SwipeRing, occupies only about 36% (254.34 mm 2 ) of the screen, almost half the Q WERTY layout.

Original: Saving screen space is important since the extra space could be used to display additional information and to make sure that the interface is not cluttered, which affects performance [23].
Revised: Saving screen space is important since the extra space could be used to display additional information and to make sure that the interface is not cluttered, which affects performance [24].

Original: For example, the extra space could be used to display the email users are responding to or more of what they have composed.
Revised: For example, the extra space could be used to display the email users are responding to or more of what they have composed.

Original: The former can keep users aware of the context.
Revised: The former can keep users aware of the context.

Original: The latter improves writing quality [55,57] and performance [17].
Revised: The latter improves writing quality [58,60] and performance [18].

Original: Numerous studies have veriﬁed this in various settings, contexts, and devices [40–42,44–46].
Revised: Numerous studies have veriﬁed this in various settings, contexts, and devices [42–44,46–49].

Original: 2.2 Facilitate Skill Transfer
Revised: 2.2 Facilitate Skill Transfer

Original: The skill acquired in using a new smartwatch keyboard is usually not transferable to other devices.
Revised: The skill acquired in using a new smartwatch keyboard is usually not transferable to other devices.

Original: This discourages users from learning a new technique.
Revised: This discourages users from learning a new technique.

Original: The keyboards that attempt to facilitate skill transfer are miniature versions of Q WERTY that are difﬁcult to use due to the small key sizes.
Revised: The keyboards that attempt to facilitate skill transfer are miniature versions of Q WERTY that are difﬁcult to use due to the small key sizes.

Original: To mitigate this, most of these keyboards relyheavily on statistical decoders, making the entry of out-ofvocabulary wordsvery difﬁcult, if not impossible.
Revised: To mitigate this, most of these keyboards rely on statistical decoders, making the entry of out-of-vocabulary words difﬁcult, or impossible.

Original: SwipeRing uses a different approach.
Revised: SwipeRing uses a different approach.

Original: Although gesture typing is much faster than tapping [29], it is not a dominant text entry method on mobile devices.
Revised: Although gesture typing is much faster than tapping [30], it is not a dominant text entry method on mobile devices.

Original: SwipeRing strategically arranges the letters in the zones to maintain gesture similarities between the gestures drawn on a virtual Q WERTY and SwipeRing to enable performing the same (or very similar) gesture to enter the same word on various devices.
Revised: SwipeRing strategi- cally arranges the letters in the zones to maintain gesture similarities between the gestures drawn on a virtual Q WERTY and SwipeRing to enable performing the same (or very similar) gesture to enter the same word on various devices.

Original: The idea is that it will encourage gesture typing by facilitating skill transfer from smartphones to smartwatches and vice versa.
Revised: The idea is that it will encour- age gesture typing by facilitating skill transfer from smartphones to smartwatches and vice versa.

Original: 2.3 Increase Usability
Revised: 2.3 Increase Usability

Original: As a result of an optimization process, the layout is strategically di- vided into seven zones, accounting for the mean contact area in index ﬁnger touch (between 28.5 and 33.5 mm 2 [53]), to facilitate comfortable and precise target selection during text entry [37].
Revised: As a result of an optimization process, the layout is strategically di- vided into seven zones, accounting for the mean contact area in index ﬁnger touch (between 28.5 and 33.5 mm 2 [56]), to facilitate comfortable and precise target selection during text entry [38].

Original: The zones range between 29.34 and 58.68 mm 2 (lengths between 9.0 and 18.
Revised: The zones range between 29.34 and 58.68 mm 2 (lengths between 9.0 and 18.

Original: mm), which are within the recommended range for target selection on both smartphones [25,30,38] and smartwatches (7.0 mm) [10].
Revised: mm), which are within the recommended range for target selection on both smartphones [26,31,39] and smartwatches (7.0 mm) [10].

Original: SwipeRing employs the whole screen for drawing gestures, which is more comfortable than drawing gestures on a miniature Q WERTY .
Revised: SwipeRing employs the whole screen for drawing gestures, which is more comfortable than drawing gestures on a miniature Q WERTY .

Original: Unlike most virtual keyboards, SwipeRing requires users to slide their ﬁngers from/to the zones instead of tapping, which also makes target selection easier.
Revised: Unlike most virtual keyboards, SwipeRing requires users to slide their ﬁngers from/to the zones instead of tapping, which also makes target selection easier.

Original: Existing work on eyes-free bezel-initiated swipe for the circular layouts revealed that the most accurate layouts have 6–8 segments [58].
Revised: Existing work on eyes-free bezel-initiated swipe for the ring-shaped layouts revealed that the most accurate layouts have 6–8 segments [61].

Original: SwipeRing enables the entry of out-of- vocabulary words through a multi-tap like an approach [9], where users repeatedly slide their ﬁngers from/to the zone that contains the target letter until the letter is entered (see Section 5.2 for further details).
Revised: SwipeRing enables the entry of out- of-vocabulary words through a multi-tap like an approach [9], where users repeatedly slide their ﬁngers from/to the zone that contains the target letter until the letter is entered (see Section 5.2 for further details).

Original: Besides, research showed that radial interfaces on circular devices visually appear to take less space even when they occupy the same area as rectangular interfaces, which not only increases clarity but also makes the interface more pleasant and attractive [43].
Revised: Besides, research showed that radial interfaces on circular devices visually appear to take less space even when they occupy the same area as rectangular interfaces, which not only increases clarity but also makes the interface more pleasant and attractive [45].

Original: 2.4 Face Agnostic
Revised: 2.4 Face Agnostic

Original: Since SwipeRing arranges the keys around the edge of a smartwatch, it works on both round and square/rectangular smartwatches.
Revised: Since SwipeRing arranges the keys around the edge of a smartwatch, it works on both round and square/rectangular smartwatches.

Original: To validate this, we investigated whether the gestures drawn on a square smartwatch and a circular smartwatch are comparable to the ones drawn on a virtual Q WERTY .
Revised: To validate this, we investigated whether the gestures drawn on a square smartwatch and a circular smartwatch are comparable to the ones drawn on a virtual Q WERTY .

Original: In a Procrustes analysis on the 10,000 most frequent words drawn on these devices, SwipeRing yielded a score of 114.81 with the square smartwatch and 118.48 with the circular smartwatch.
Revised: A Procrustes analysis on the 10,000 most frequent words drawn on these devices, SwipeRing yielded a score of 114.81 with the square smartwatch and 118.48 with the circular smartwatch.

Original: This suggests that the gestures drawn on these devices are very similar.
Revised: This suggests that the gestures drawn on these devices are very similar.

Original: In fact, the square smartwatch yielded a slightly better score than the circular smartwatch (the smaller the score the better the similarity, Section 4.2), most likely because the shapes of these devices are similar (Fig. 3).
Revised: In fact, the square smartwatch yielded a slightly better score than the circular smartwatch (the smaller the score, the better the similarity, Section 4.2), most likely because the shapes of these devices are similar (Fig. 3).

Original: 3 R ELATED W ORK
Revised: 3 R ELATED W ORK

Original: This section covers the most common text entry techniques for smartwatches.
Revised: This section covers the most common text entry techniques for smartwatches.

Original: Tables 1 and 2 summarize the performance of some of these techniques.
Revised: Tables 1 and 2 summarize the performance of some of these techniques.

Original: For a comprehensive review of existing text entry techniques for smartwatches, we refer to Arif et al.
Revised: For a comprehensive review of existing text entry techniques for smartwatches, we refer to Arif et al.

Original: 3.1 Q WERTY Layout
Revised: 3.1 Q WERTY Layout

Original: Most text entry techniques for smartwatches are miniature versions of the standard Q WERTY that use multi-step approaches to increase the target area.
Revised: Most text entry techniques for smartwatches are miniature versions of the standard Q WERTY that use multi-step approaches to increase the target area.

Original: ZoomBoard [36] displays a miniature Q WERTY that enables iterative zooming to enlarge regions of the keyboard for comfortable tapping.
Revised: ZoomBoard [37] displays a miniature Q WERTY that enables iterative zooming to enlarge regions of the keyboard for comfortable tapping.

Original: SplitBoard [18] displays half of a Q WERTY keyboard so that the keys are large enough for tapping.
Revised: SplitBoard [19] displays half of a Q WERTY keyboard so that the keys are large enough for tapping.

Original: Users ﬂick left and right to see the other half of the keyboard.
Revised: Users ﬂick left and right to see the other half of the keyboard.

Original: SwipeBoard [5] requires two swipes to enter one letter, the ﬁrst to select theregion of the target letter  and the second towards the letter to enter it.
Revised: SwipeBoard [5] requires two swipes to enter one letter, the ﬁrst to select the target letter region and the second towards the letter to enter it.

Original: DriftBoard [47] is a movable miniature Q WERTY with a ﬁxed cursor point.
Revised: DriftBoard [50] is a movable miniature Q WERTY with a ﬁxed cursor point.

Original: To enter text, users drag the keyboard to position the intended key  within the cursor point.
Revised: To enter text, users drag the keyboard to position the intended key [Table] within the cursor point.

Original: Some miniature Q WERTY keyboards use powerful statistical decoders to account for the“fat-ﬁnger problem” [52].
Revised: Some miniature Q WERTY keyboards use powerful statistical decoders to account for the“fat-ﬁnger problem” [55].

Original: WatchWriter [13] appropriates a smartphone Q WERTY for smartwatches.
Revised: WatchWriter [13] appropriates a smartphone Q WERTY for smartwatches.

Original: It supports both predictive tap and gesture typing.
Revised: It supports both predictive tap and gesture typing.

Original: Yi et al.
Revised: Yi et al.

Original: [59] uses a similar approach with even smaller keyboards ( and 35 mm).
Revised: [62] uses a similar approach with even smaller keyboards (30 and 35 mm).

Original: VelociWatch [51] also uses a statistical decoder, but enables users to lock in particular letters of their input to disable potential auto-corrections.
Revised: VelociWatch [54] also uses a statistical decoder, but enables users to lock in particular letters of their input to disable potential auto-corrections.

Original: Some techniques use variants of the standard Q WERTY layout.
Revised: Some techniques use variants of the standard Q WERTY layout.

Original: ForceBoard [19] maps Q WERTY to a [Table] grid by assigning two letters to each key.
Revised: ForceBoard [20] maps Q WERTY to a 3 × 5 grid by assigning two letters to each key.

Original: Applying different levels of force on the keys enters the respective letters.
Revised: Applying different levels of force on the keys enters the respective letters.

Original: DualKey [15] uses a similar layout, but requires users to tap with different ﬁngers to disambiguate the input.
Revised: DualKey [16] uses a similar layout, but requires users to tap with different ﬁngers to disambiguate the input.

Original: It uses external hardware to differentiate between the ﬁngers.
Revised: It uses external hardware to differentiate between the ﬁngers.

Original: DiaQ WERTY [27] uses diamond-shaped keys to ﬁt Q WERTY in a round smartwatch at 10:7 aspect ratio.
Revised: DiaQ WERTY [28] uses diamond-shaped keys to ﬁt Q WERTY in a round smartwatch at 10:7 aspect ratio.

Original: OptimalT9 [39] maps Q WERTY to a 3 × 3 grid, then disambiguates input using a statistical decoder.
Revised: Optimal- T9 [40] maps Q WERTY to a 3 × 3 grid, then disambiguates input using a statistical decoder.

Original: These techniques, however, occupy a large area of the screen real-estate, require multiple actions to enter one letter, or useaggressive prediction models that make the entry  out-of-vocabulary words very difﬁcult, often impossible.
Revised: These techniques, however, occupy a substantial area of the screen real-estate, require multiple actions to enter one letter, or use prediction models that make the entry of out-of-vocabulary words difﬁcult.

Original: 3.2 Other Layouts
Revised: 3.2 Other Layouts

Original: There are a few techniques that use different layouts.
Revised: There are a few techniques that use different layouts.

Original: Dunlop et al.
Revised: Dunlop et al.

Original: [10] and Komninos and Dunlop [28] map an alphabetical layout to six ambiguous keys, then uses a statistical decoder to dis- ambiguate input.
Revised: [10] and Komninos and Dunlop [29] map an alphabetical layout to six ambiguous keys, then uses a statistical decoder to dis- ambiguate input.

Original: It enables contextual word suggestions and word completion.
Revised: It enables contextual word suggestions and word completion.

Original: QLKP [18] (initially designed for smartphones [20]) maps a Q WERTY -like layout to a 3 × 3 grid.
Revised: QLKP [19] (initially designed for smartphones [21]) maps a Q WERTY -like layout to a 3 × 3 grid.

Original: Similar to multi-tap [9], users tap on a key repeatedly until they get the intended letter.
Revised: Similar to multi-tap [9], users tap on a key repeatedly until they get the intended letter.

Original: These techniques also occupy a large area ofthe touchscreen real-estate and require multiple actions to enter one letter.
Revised: These techniques also occupy a substantial area of touchscreen real-estate and require multiple actions to enter one letter.

Original: 3.3 Circular Layouts
Revised: 3.3 Ring-Shaped Layouts

Original: There are some circular keyboards available for smartwatches.
Revised: There are some ring-shaped keyboards available for smartwatches.

Original: In- clineType [16] places an alphabetical layout around the edge of the devices.
Revised: InclineType [17] places an alphabetical layout around the edge of the devices.

Original: To enter a letter, users ﬁrst select the letter by moving the wrist, then tap on the screen.
Revised: To enter a letter, users ﬁrst select the letter by moving the wrist, then tap on the screen.

Original: COMPASS [60] also uses an alphabetical layout, but does not use touch interaction.
Revised: COMPASS [63] also uses an alphabetical layout, but does not use touch interaction.

Original: To enter text, users rotate the bezel to place one of the three available cursors on the desired letter, then press a button on the side of the watch.
Revised: To enter text, users rotate the bezel to place one of the three available cursors on the desired letter, then press a button on the side of the watch.

Original: WrisText [12] is a one-handed technique, with which users enter text by whirling the wrist of the hand towards six directions, each representing a key in a circular keyboard with the letters in alphabetical order.
Revised: WrisText [12] is a one-handed technique, with which users enter text by whirling the wrist of the hand towards six directions, each representing a key in a ring-shaped keyboard with the letters in alphabetical order.

Original: BubbleFlick [49] is a circular keyboard for Japanese text entry.
Revised: BubbleFlick [52] is a ring-shaped keyboard for Japanese text entry.

Original: It enables text entry through two actions.
Revised: It enables text entry through two actions.

Original: Users ﬁrst touch a key, which partitions the circular area inside the layout into four radial areas for the four kana letters on the key.
Revised: Users ﬁrst touch a key, which partitions the ring-shaped area inside the layout into four radial areas for the four kana letters on the key.

Original: Users then stroke towards the intended letter to enter it.
Revised: Users then stroke towards the intended letter to enter it.

Original: A commercial product, TouchOne Keyboard Wear [50] divides an alphabetical layout into eight zones to let users enter text using a T9-like [14]  approach.
Revised: A commercial product, TouchOne Keyboard Wear [53] divides an alphabetical layout into eight zones to let users enter text using a T9-like [14] [Table] approach.

Original: It enables the entry of out-of-vocabulary words using an approach similar to BubbleFlick.
Revised: It enables the entry of out-of-vocabulary words using an approach similar to BubbleFlick.

Original: Go et al.
Revised: Go et al.

Original: [11] designed an eyes-free text entry technique that enables users to EdgeWrite [56] on a smartwatch with the support of auditory feedback.
Revised: [11] designed an eyes-free text entry technique that enables users to EdgeWrite [59] on a smartwatch with the support of auditory feedback.

Original: Most of these techniques use a sequence of actions or a statistical decoder to disambiguate the input.
Revised: Most of these techniques use a sequence of actions or a statistical decoder to disambiguate the input.

Original: Besides, most of these techniques are standalone, hence the skills acquired in using these keyboards are usually not usable on other devices.
Revised: Besides, most of these techniques are standalone, hence the skills acquired in using these keyboards are usually not usable on other devices.

Original: [Table] InclineType [Table] Cirrin [34] is a pen-based word-level text entry technique for PDAs.
Revised: Cirrin [35] is a pen-based word-level text entry technique for PDAs.

Original: It uses a novel circular layout with dedicated keys for each letter.
Revised: It uses a novel ring-shaped layout with dedicated keys for each letter.

Original: To enter a word, users pass their pen through the intended letters.
Revised: To enter a word, users pass their pen through the intended letters.

Original: This approach has also been used on other devices [24].
Revised: This approach has also been used on other devices [25].

Original: C-Q WERTY is a similar technique [7], but differs in letter arrange- ment, which is based on the Q WERTY layout.
Revised: C-Q WERTY is a similar technique [7], but differs in letter arrange- ment, which is based on the Q WERTY layout.

Original: To enter a word with
Revised: To enter a word with

Original: C-Q WERTY , users either tap on the letters in the word individually or drag the ﬁnger over them in sequence.
Revised: C-Q WERTY , users either tap on the letters in the word individually or drag the ﬁnger over them in sequence.

Original: While there are some similarities between Cirrin, C-Q WERTY , and SwipeRing, the ap- proach employed in the latter technique is fundamentally different.
Revised: While there are some similarities between Cirrin, C-Q WERTY , and SwipeRing, the approach employed in the latter technique is fundamentally different.

Original: First, SwipeRing divides the layout into seven zones, thus does not require precise selection of the letters, but much larger zones.
Revised: First, SwipeRing divides the layout into seven zones, thus does not require precise selection of the letters, but much larger zones.

Original: Both Cirrin and C-Q WERTY , in contrast, use individual keys for each letter, thus require  precise selection of the keys.
Revised: Both Cirrin and C-Q WERTY , in contrast, use individual keys for each letter, thus require a precise selection of the keys.

Original: Second, like gesture typing on a smartphone, SwipeRing does not require users to go over the same letter (or the letters on the same zone) repeatedly if they appear in a word multiple times in sequence (such as, “oo” in “book”).
Revised: Second, like gesture typing on a smartphone, SwipeRing does not require users to go over the same letter (or the letters on the same zone) repeatedly if they appear in a word multiple times in sequence (such as, “oo” in “book”).

Original: But both Cirrin and C-Q WERTY require users to go over the same letter repeatedly in such cases by sliding the ﬁnger out of the keyboard then sliding back to the key.
Revised: But both Cirrin and C-Q WERTY require users to go over the same letter repeatedly in such cases by sliding the ﬁnger out of the keyboard then sliding back to the key.

Original: We found out users use this strategy also for entering letters that have the respective keys placed side-by-side, as they are difﬁcult to select consecutively due to the smaller size.
Revised: We found out users use this strategy also for entering letters that have the respective keys placed side-by-side, as they are difﬁcult to select consecutively due to the smaller size.

Original: Finally, SwipeRing is optimized to maintain gesture similarities between SwipeRing and a virtual Q WERTY to facilitate skill transfer between devices.
Revised: Finally, SwipeRing is optimized to maintain gesture similarities between SwipeRing and a virtual Q WERTY to facilitate skill transfer between devices.

Original: Cirrin and C-Q WERTY do not account for this.

Original: 4 L AYOUT O PTIMIZATION
Revised: 4 L AYOUT O PTIMIZATION

Original: SwipeRing maps the standard Q WERTY layout to a ring around the edge of a smartwatch (Fig. 1).
Revised: SwipeRing maps the standard Q WERTY layout to a ring around the edge of a smartwatch (Fig. 1).

Original: It places the left and the righthand keys of Q WERTY [35] to the left and right sides of the layout, respectively.
Revised: It places the left and the righthand keys of Q WERTY [36] to the left and right sides of the layout, respectively.

Original: Likewise, the top, home, and bottom row keys of
Revised: Likewise, the top, home, and bottom row keys of

Original: Q WERTY are placed at the top, middle, and bottom parts of the layout, respectively.
Revised: Q WERTY are placed at the top, middle, and bottom parts of the layout, respectively.

Original: Each letter is positioned at multiple of 360 ◦ / 26, resulting in an angular step of 13.80 ◦ , starting with the letter ‘q’ at 180 ◦ .
Revised: Each letter is positioned at multiple of 360 ◦ / 26, resulting in an angular step of 13.80 ◦ , starting with the letter ‘q’ at 180 ◦ .

Original: This design was adapted to maintain a likeness to Q WERTY to exploit the widespread familiarity with the keyboard to facilitate learning [20,39].
Revised: This design was adapted to maintain a likeness to Q WERTY to exploit the widespread familiarity with the keyboard to facilitate learning [21,40].

Original: We then grouped the letters into zones to improve the usability of the keyboard by facilitating precise target selection, further discussed in Section 4.3.
Revised: We then grouped the letters into zones to improve the usability of the keyboard by facilitating precise target selection, further discussed in Section 4.3.

Original: In practice, the letters can be grouped in numerous different ways, resulting in a set of possible layouts L .
Revised: In practice, the letters can be grouped in numerous different ways, resulting in a set of possible layouts L .

Original: However, the purpose here was to identify a particular layout l ∈ L that ensures that the gestures drawn on the layout l are similar to the ones drawn on a virtual Q WERTY .
Revised: However, the purpose here was to identify a particular layout l ∈ L that ensures that the gestures drawn on the layout l are similar to the ones drawn on a virtual Q WERTY .

Original: This requires searching for an optimal letter grouping that maximizes gesture similarity.
Revised: This requires searching for an optimal letter grouping that maximizes gesture similarity.

Original: We introduce the following notation to formally deﬁne the optimization procedure.
Revised: We introduce the following notation to formally deﬁne the optimization procedure.

Original: Let g Q ( w ) be the gesture used to enter a word w on the virtual Q WERTY and g SwipeRing ( w ; l ) be the gesture used to enter the word w on the layout l of SwipeRing.
Revised: Let g Q ( w ) be the gesture used to enter a word w on the virtual Q WERTY and g SwipeRing ( w ; l ) be the gesture used to enter the word w on the layout l of SwipeRing.

Original: Instead of maximizing the similarity between the gestures, we can equivalently minimize the discrepancy between the gestures, which we measure using a function ψ .
Revised: Instead of maximizing the similarity between the gestures, we can equivalently minimize the discrepancy between the gestures, which we measure using a function ψ .

Original: Then, our problem is to ﬁnd the layout l that minimizes the following loss function L :
Revised: Then, our problem is to ﬁnd the layout l that minimizes the following loss function L :

Original: [Equation]
Revised: [Equation]

Original: Here, the dissimilarity between the gestures is weighted by the probability of the occurrence of the word to assure that the gestures for the most frequent words are the most similar.
Revised: Here, the dissimilarity between the gestures is weighted by the probability of the occurrence of the word to assure that the gestures for the most frequent words are the most similar.

Original: To efﬁciently optimize this problem, we made several modeling assumptions and simpliﬁcations, which are discussed in the following sections.
Revised: We made several modeling assumptions and simpliﬁcations to efﬁciently optimize this problem, which are discussed in the following sections.

Original: 4.1 Gesture Modelling
Revised: 4.1 Gesture Modelling

Original: We model each gesture as a piece-wise linear curve connecting the letters on a virtual Q WERTY or the zones on SwipeRing.
Revised: We model each gesture as a piece-wise linear curve connecting the letters on a virtual Q WERTY or the zones on SwipeRing.

Original: Therefore, the gesture for a word composed of n letters can be seen as a 2 × n dimensional matrix (Fig. 4), where each column contains coordinates ( x , y ) of the corresponding letter.
Revised: Therefore, the gesture for a word composed of n letters can be seen as a 2 × n dimensional matrix (Fig. 4), where each column contains coordinates ( x , y ) of the corresponding letter.

Original: To simulate the drawn gesture on a virtual Q WERTY for the word w , denoted as g Q ( w ) , we connect the centers of the corresponding keys of the default Android
Revised: To simulate the drawn gesture on a virtual Q WERTY for the word w , denoted as g Q ( w ) , we connect the centers of the corresponding keys of the default Android

Original: Q WERTY on a Motorola G 5 smartphone (24 cm 2 keyboard area) 1 , producing unique gestures for each word.
Revised: Q WERTY on a Motorola G 5 smartphone (24 cm 2 keyboard area) 1 , producing unique gestures for each word.

Original: With SwipeRing, how- ever, we account for the fact that a word can have multiple gestures forming a set G SwipeRing ( w ; l ) .
Revised: With SwipeRing, how- ever, we account for the fact that a word can have multiple gestures forming a set G SwipeRing ( w ; l ) .

Original: The zones containing 4–6 letters are wide enough to enable initiating a gesture either at the center, left, or right side of the zone (Fig. 5), resulting in multiple possibilities.
Revised: The zones containing 4–6 letters are wide enough to enable initiating a gesture either at the center, left, or right side of the zone (Fig. 5), resulting in multiple possibilities.

Original: Therefore, we set the gesture g SwipeRing ( w ; l ) to be the one that has minimal difference when compared to the gesture drawn on the virtual Q WERTY measured by discrepancy function ψ :
Revised: Therefore, we set the gesture g SwipeRing ( w ; l ) to be the one that has minimal difference when compared to the gesture drawn on the virtual Q WERTY measured by discrepancy function ψ :

Original: [Equation]
Revised: [Equation]

Original: 4.2 Discrepancy Function
Revised: 4.2 Discrepancy Function

Original: For the discrepancy function ψ ( g 1 , g 2 ) between gestures g 1 and g 2 , our requirement is to have a rotation and scale agnostic measure that attains a value of 0 if and only if g 2 is a rotated and re-scaled version of g 1 .
Revised: For the discrepancy function ψ ( g 1 , g 2 ) between gestures g 1 and g 2 , our requirement is to have a rotation and scale agnostic measure that attains a value of 0 if and only if g 2 is a rotated and re-scaled version of g 1 .

Original: One possible form of such ψ function can be deﬁned as yet another optimization problem of:
Revised: One possible form of such ψ function can be deﬁned as yet another optimization problem of:

Original: [Equation]
Revised: [Equation]

Original: Where α and R are the rescaling factor and the rotation matrix applied to a gesture, respectively, while (cid:107) .
Revised: Where α and R are the rescaling factor and the rotation matrix applied to a gesture, respectively, while (cid:107) .

Original: (cid:107) F is the Frobenius norm 2 .
Revised: (cid:107) F is the Frobenius norm 2 .

Original: We recognize Equation 3 as an Ordinary Procrustes analysis problem, the solution of which is given in closed-form by Singular Value
Revised: We recognize Equation 3 as an Ordinary Procrustes analysis problem, the solution of which is given in closed-form by Singular Value

Original: Decomposition [8].
Revised: Decomposition [8].

Original: Note that the value of ψ ( g 1 , g 2 ) is within the range of [ 0 , ∞ ] .
Revised: Note that the value of ψ ( g 1 , g 2 ) is within the range of [ 0 , ∞ ] .

Original: Additionally, we restrict the rotation within the range of ± 45 ◦ since SwipeRing gestures that are rotated more than 45 ◦ in either direction are unlikely to look similar to their virtual Q WERTY counterparts (Fig. 6).Best Match Average Match Worst Match
Revised: Additionally, we restrict the rotation within the range of ± 45 ◦ since SwipeRing gestures that are rotated more than 45 ◦ in either direction are unlikely to look similar to their virtual Q WERTY counterparts (Fig. 6).

Original: [Equation]

Original: [Figure]

Original: [Equation]

Original: [Figure]

Original: [Equation]

Original: [Figure]

Original: [Equation]

Original: [Figure]

Original: [Equation]

Original: [Figure]

Original: [Equation]

Original: [Figure]
Revised: [Figure]

Original: “the”

Original: 4.3 Enumeration of All Possible Letter Groupings
Revised: 4.3 Enumeration of All Possible Letter Groupings

Original: The total number of possible letter groupings, and thus layouts, de- pends on how large we allow the groups to be.
Revised: The total number of possible letter groupings, and thus layouts, depends on how large we allow the groups to be.

Original: To determine this, we conducted a literature review of ambiguous keyboards that use linguistic models for decoding the input to ﬁnd out whether the number of letters assigned per key or zone (the level of ambiguity) affects the performance of a keyboard.
Revised: To determine this, we conducted a literature review of ambiguous keyboards that use linguistic models for decoding the input to ﬁnd out whether the number of letters assigned per key or zone (the level of ambiguity) affects the performance of a keyboard.

Original: Table 3 displays an excerpt of our review, where one can seethat there is a “somewhat” inverse relationship between the level of ambiguity and entry speed.
Revised: Table 3 displays an excerpt of our review, where one can see “somewhat” inverse relationship between the level of ambiguity and entry speed.

Original: Keyboards that assigna fewernumber of letters per key or zone yield a relatively better entry speed than the ones that have more letters per key or zone.
Revised: Keyboards that assign fewer letters per key or zone yield a relatively better entry speed than those with more letters per key or zone.

Original: Based on this, we decided to assign 3–6 letters per zone.
Revised: Based on this, we decided to assign 3–6 letters per zone.

Original: Although, this alone cannot determine the appropriate number of letters in each key since the performance of a keyboard depends on other factors, such as the layout and the reliability of the decoder, it gives a rough estimate.
Revised: Although, this alone cannot determine the appropriate number of letters in each key since the performance of a keyboard depends on other factors, such as the layout and the reliability of the decoder, it gives a rough estimate.

Original:  Next, we discovered all possible shatterings of the circular string { qwertyuiophjklmnbvcxzgfdsa } into substrings of length 3– letters each, resulting in 4,355 different layouts in total, which constitute our search set L .
Revised: [Table] Next, we discovered all possible shatterings of the ring-shaped string { qwertyuiophjklmnbvcxzgfdsa } into substrings of length 3–6 letters each, resulting in 4,355 different layouts in total, which constitute our search set L .

Original: Each possible shattering, such as[Table] [Table] { qwer }{ tyu }{ iophjk }{ lmnbv }{ cxzg }{ fdsa } , represents one possible layout.
Revised: Each possible shattering, such as { qwer }{ tyu }{ iophjk }{ lmnbv }{ cxzg }{ fdsa } , represents one possible layout.

Original: We tested several of these layouts on a small smartwatch (9.3 cm 2 circular display) to investigate if the zones containing three letters are wide enough for precise target selection.
Revised: We tested several of these layouts on a small smartwatch (9.3 cm 2 circular display) to investigate if the zones containing three letters are wide enough for precise target selection.

Original: Results showed that the zones range between 29.0 and 57.5 mm(lengths between 9.0 and 18.0 mm), which are within the length recommended for target selection on both smartphones [25,30,38] and smartwatches (7.0 mm) [10].
Revised: Results showed that the zones range between 29.0 and 57.5 mm(lengths between 9.0 and 18.0 mm), which are within the length recommended for target selection on both smartphones [26,31,39] and smartwatches (7.0 mm) [10].

Original: Fig.
Revised: Fig.

Original: 7 illustrate some of these layouts.
Revised: 7 illustrate some of these layouts.

Original: [Figure]
Revised: [Figure]

Original: 4.4 Algorithm
Revised: 4.4 Algorithm

Original: To ﬁnd the optimal layout, we simulated billions of gestures for the 10,000 most frequent words in the English language [54] on the 4,355 possible segmented SwipeRing layouts 3 .
Revised: To ﬁnd the optimal layout, we simulated billions of gestures for the 10,000 most frequent words in the English language [57] on the 4,355 possible segmented SwipeRing layouts 3 .

Original: We then matched the gestures produced for each word on each layout with the gestures produced on a virtual Q WERTY using  Procrustes analysis to pick the layout that yielded the best match score (118.48).
Revised: We then matched the gestures produced for each word on each layout with the gestures produced on a virtual Q WERTY using the Procrustes analysis to pick the layout that yielded the best match score (118.48).

Original: The ﬁnal layout (Fig. 1) scored, on average, 1.27 times better Procrustes value compared to the other possible layouts.
Revised: The ﬁnal layout (Fig. 1) scored, on average, 1.27 times better Procrustes value compared to the other possible layouts.

Original: optimal layout l .

Original: Input: Possible grouping layouts L = { l 1 , l 2 ,... } , word corpus W = { w 1 , w 2 ,... } Function OptLayout( L , W ) :

Original: L min ← ∞ , l min ← ∞ for layout l ∈ L do

Original: L ←for word w ∈ W do

Original: L ← L + p ( w ) ψ (cid:16) g Q ( w ) , g SwipeRing ( w ; l ) (cid:17) endif L ≤ L min then L min ← L , l min ← l end end

Original: 5 K EYBOARD F EATURES
Revised: 5 K EYBOARD F EATURES

Original: This section describes some key features of the proposed keyboard.
Revised: This section describes some key features of the proposed keyboard.

Original: 5.1 Decoder
Revised: 5.1 Decoder

Original: We developed a simple decoder to suggest corrections andto display the most probable words in a suggestion bar.
Revised: We developed a simple decoder to suggest corrections and display the most probable words in a suggestion bar.

Original: For this, we used a combination of a character-level language model and a word-level bigram model for the next word prediction.
Revised: For this, we used a combination of a character-level language model and a word-level bigram model for the next word prediction.

Original: To this end, we calculate the conditional probability of the user typing the word w given that the previous word was wn − 1 and the current zone sequence is s : 
Revised: To this end, we calculate the conditional probability of the user typing the word w given that Input: Possible grouping layouts L = { l 1 , l 2 ,... } , word corpus W = { w 1 , w 2 ,... } Function OptLayout( L , W ) : L min ← ∞ , l min ← ∞ for layout l ∈ L do L ←for word w ∈ W do

Revised: L ← L + p ( w ) ψ (cid:16) g Q ( w ) , g SwipeRing ( w ; l ) (cid:17) endif L ≤ L min then L min ← L , l min ← l end end the previous word was w n − 1 and the current zone sequence is s :

Original: [Equation]
Revised: [Equation]

Original: Here, M ( w ) is the sequence of zones that the user must gesture over to enter the word w with SwipeRing, match ( s 1 , s 2 ) is the in- dicator function that returns 1 if s 2 is a preﬁx of s 1 or 0 otherwise, and count ( w n , w n − 1 ) is the number of occurrences of a bigram ( w n , w n − 1 ) in the training corpus.
Revised: Here, M ( w ) is the sequence of zones that the user must gesture over to enter the word w with SwipeRing, match ( s 1 , s 2 ) is the in- dicator function that returns 1 if s 2 is a preﬁx of s 1 or 0 otherwise, and count ( w n , w n − 1 ) is the number of occurrences of a bigram ( w n , w n − 1 ) in the training corpus.

Original: To predict the most probable word for a given zone sequence s and previous word w n − 1 , we compute argmax w P ( w n = w | s , w n − 1 ) using the preﬁx tree (Trie) data structure.
Revised: To predict the most probable word for a given zone sequence s and previous word w n − 1 , we compute argmax w P ( w n = w | s , w n − 1 ) using the preﬁx tree (Trie) data structure.

Original: This implementation can output k highest probable words, which we display in the suggestion bar.
Revised: This implementation can output k highest probable words, which we display in the suggestion bar.

Original: When no word has been typed yet, we use a unigram reduction of the model, otherwise we use the bigram model trained on the COCA corpus [6].
Revised: When no word has been typed yet, we use a unigram reduction of the model, otherwise, we use the bigram model trained on the COCA corpus [6].

Original: Due to the limited memory capacity of the smartwatch, the Trie uses the 1,300 most probable bigrams: bigram models scale as the square of the number of words, thus quickly outrun the available memory on the device.
Revised: Due to the limited memory capacity of the smartwatch, the Trie uses the 1,300 most probable bigrams: bigram models scale as the square of the number of words, thus quickly outrun the available memory on the device.

Original: If the Trie does not have a bigram containing the user’s previous word w n − 1 , we revert to the unigram predictions.
Revised: If the Trie does not have a bigram containing the user’s previous word w n − 1 , we revert to the unigram predictions.

Original: Our language model is fairly simple, and more advanced models (involving neural nets, for instance) can be created.
Revised: Our language model is fairly simple, and more advanced models (involving neural nets, for instance) can be created.

Original: However, devising efﬁcient language models for new keyboards is a research problem on its own and beyond the scope of our paper.
Revised: However, devising efﬁcient language models for new keyboards is a research problem on its own and beyond the scope of our paper.

Original: After obtaining the list of the most probable words, SwipeRing places up to 10 most probable words in the suggestion bar, automatically positioned in close proximity to the input area (Fig. 1).
Revised: After obtaining the list of the most probable words, SwipeRing places up to 10 most probable words in the suggestion bar, automatically positioned in close proximity to the input area (Fig. 1).

Original: The suggestion bar automatically updates as the user continues gesturing.
Revised: The suggestion bar automatically updates as the user continues gesturing.

Original: Once done, the most probable word from the list is entered.
Revised: Once done, the most probable word from the list is entered.

Original: The user could select a different word from the suggestion bar by tapping on it, which replaces the last entered word.
Revised: The user could select a different word from the suggestion bar by tapping on it, which replaces the last entered word.

Original: Although the user can only see 2–4 words in the suggestion bar due to the smaller screen, she can swipe left and right on the bar to see the remaining words.
Revised: Although the user can only see 2–4 words in the suggestion bar due to the smaller screen, she can swipe left and right on the bar to see the remaining words.

Original: 5.2 One-Letter and Out-of-Vocabulary (OOV) Words
Revised: 5.2 One-Letter and Out-of-Vocabulary (OOV) Words

Original: SwipeRing enables the entry of one-letter and out-of-vocabulary words through repeated taps or strokes from/to the zones containing the target letters.
Revised: SwipeRing enables the entry of one-letter and out-of-vocabulary words through repeated taps or strokes from/to the zones containing the target letters.

Original: The keyboard ﬁrst enters the two one-letter words in the English language “a” and “I”, then the other letters in the sequence in which they appear in the zones, like multi-tap [9].
Revised: The keyboard ﬁrst enters the two one-letter words in the English language “a” and “I”, then the other letters in the sequence in which they appear in the zones, like multi-tap [9].

Original: For instance, to enter the letter ‘e’, which is in the top-right zone containing the letters: ‘q’, ‘w’, ‘e’, and ‘r’ (Fig. 1), the user taps or slides the ﬁnger three times from the middle area to the zone or from the edge to the middle area (Fig. 8).
Revised: For instance, to enter the letter ‘e’, which is in the top-right zone containing the letters: ‘q’, ‘w’, ‘e’, and ‘r’ (Fig. 1), the user taps or slides the ﬁnger three times from the middle area to the zone or from the edge to the middle area (Fig. 8).

Original: 5.3 Error Correction and Special Characters
Revised: 5.3 Error Correction and Special Characters

Original: SwipeRing automatically enters a space when a word is predicted or manually selected from the suggestion bar.
Revised: SwipeRing automatically enters a space when a word is predicted or manually selected from the suggestion bar.

Original: During character-level text entry (to enter out-of-vocabulary words), users enter space by performing a right stroke inside the empty area of the keyboard.
Revised: During character-level text entry (to enter out-of-vocabulary words), users enter space by performing a right stroke inside the empty area of the keyboard.

Original: Tapping on the transcribed text deletes the last entry,which could be either a word or a letter.
Revised: Tapping on the transcribed text deletes the last entry, either a word or a letter.

Original: The keyboard performs a carriage return or an enter action when the user double-taps on the screen.
Revised: The keyboard performs a carriage return or an enter action when the user double-taps on the screen.

Original: Currently, SwipeRing does not supportthe entry of uppercase letters, special symbols, numbers, and languages other than English.
Revised: Currently, SwipeRing does not support uppercase letters, special symbols, numbers, and languages other than English.

Original: However,support for these could be easily added by enabling the user to longpress or dwell on the screen or the zones to switch back and forth between the cases and change the layout for digits and symbols.
Revised: However, these could be easily added by enabling the user to long-press or dwell on the screen or the zones to switch back and forth between the cases and change the layout for digits and symbols.

Original: Note that the evaluation of novel text entry techniques without the support for numeric and special characters is common practice since it eliminates a potential confound [33].
Revised: Note that the evaluation of novel text entry techniques without the support for numeric and special characters is common practice since it eliminates a potential confound [34].

Original: 6 U SER S TUDY
Revised: 6 U SER S TUDY

Original: We conducted a user study to compare SwipeRing with C-Q WERTY .
Revised: We conducted a user study to compare SwipeRing with C-Q WERTY .

Original: C-Q WERTY uses almost the same layout as SwipeRing but places ‘g’ at the NE corner, while SwipeRing places it at the SW corner (left side of the layout since ‘g’ on Q WERTY is usually pressed with the left hand).
Revised: C-Q WERTY uses almost the same layout as SwipeRing but places ‘g’ at the NE corner, while SwipeRing places it at the SW corner (left side of the layout since ‘g’ on Q WERTY is usually pressed with the left hand).

Original: Both layouts share the deign goal of maintaining a simi- larity to Q WERTY by using the touch-typing metaphor of physical keyboards (keys assigned to different hands).
Revised: Both layouts share the design goal of maintaining sim- ilarity to Q WERTY by using the touch-typing metaphor of physical keyboards (keys assigned to different hands).

Original: This likely resulted in similar (but nonidentical) layouts.
Revised: This likely resulted in similar (but nonidentical) layouts.

Original: Studies showed that using a physical analogy/metaphor like this enables novices to learn a method faster by skill transfer [32, pp.
Revised: Studies showed that using a physical analogy/metaphor like this enables novices to learn a method faster by skill transfer [33, pp.

Original: Besides, C-Q WERTY does not divide the keys into zones, optimize them for gesture typing and skill transfer, and uses  slightly different mechanism for gesture drawing approaches for the two are also different (Section 3.3).We will clarify this.
Revised: Besides, C-Q WERTY does not divide the keys into zones, optimize them for gesture typing and skill transfer, and uses a slightly different mechanism for gesture drawing approaches for the two are also different (Section 3.3).

Original: Hence, a comparison between the two will highlight the performance difference due to the contributions of this work.
Revised: Hence, a comparison between the two will highlight the performance difference due to the contributions of this work.

Original: 6.1 Apparatus
Revised: 6.1 Apparatus

Original: We used an LG Watch Style smartwatch, 42.3 × 45.7 × 10.8 mm, 9.3 cm 2 circular display, 46 grams, running on the Wear OS at 360 ×pixels in the study (Fig. 9).
Revised: We used an LG Watch Style smartwatch, 42.3 × 45.7 × 10.8 mm, 9.3 cm 2 circular display, 46 grams, running on the Wear OS at 360 ×pixels in the study (Fig. 9).

Original: We decided to use a circular watch in the study since it is the most popular shape for (smart)watches [22,26].
Revised: We decided to use a circular watch in the study since it is the most popular shape for (smart)watches [23,27].

Original: We developed SwipeRing with the Android Studio 3.4.2, SDK 28.
Revised: We developed SwipeRing with the Android Studio 3.4.2, SDK 28.

Original: We collected the original source code of C-Q WERTY from
Revised: We collected the original source code of C-Q WERTY from

Original: Costagliola et al.
Revised: Costagliola et al.

Original: [7], which was also developed for the Wear OS.
Revised: [7], which was also developed for the Wear OS.

Original: Both applications calculated all performance metrics directly and logged all interactions with timestamps.
Revised: Both applications calculated all performance metrics directly and logged all interactions with timestamps.

Original: 6.2 Design
Revised: 6.2 Design

Original: We used a between-subjects design to avoid interference between the conditions.
Revised: We used a between-subjects design to avoid interference between the conditions.

Original: Since both techniques use similar layouts, the skill acquired while learning one technique would have affected perfor- mance with the other technique [32].
Revised: Since both techniques use similar layouts, the skill acquired while learning one technique would have affected perfor- mance with the other technique [33].

Original: There were separate groups of twelve participants for C-Q WERTY and SwipeRing.
Revised: There were separate groups of twelve participants for C-Q WERTY and SwipeRing.

Original: Each group used the respective technique to enter short English phrases in eight  blocks.
Revised: Each group used the respective technique to enter short English phrases in eight [Table] [Table] blocks.

Original: Each block contained 10 random phrases from a set [33].
Revised: Each block contained 10 random phrases from a set [34].

Original: Hence, the design was as follows.
Revised: Hence, the design was as follows.

Original: 2 groups [Table] and SwipeRing [Table]
Revised: 2 groups: C-Q WERTY and SwipeRing × 12 participants × 8 blocks × 10 random phrases = 1,920 phrases in total.

Original: [Table]

Original: [Table]

Original: 6.3 Participants
Revised: 6.3 Participants

Original: Twenty-four participants took part in the user study.
Revised: Twenty-four participants took part in the user study.

Original: They were divided into two groups.
Revised: They were divided into two groups.

Original: Table 4, 5 present the demographics of these groups.
Revised: Table 4, 5 present the demographics of these groups.

Original: Almost all participants chose to wear the smartwatch on their left hand and perform the gestures using the index ﬁnger of the right hand (Fig. 9).
Revised: Almost all participants chose to wear the smartwatch on their left hand and perform the gestures using the index ﬁnger of the right hand (Fig. 9).

Original: All participants were proﬁcient in the English language.
Revised: All participants were proﬁcient in the English language.

Original: In both groups, three participants identiﬁed themselves as experienced gesture typists.
Revised: In both groups, three participants identiﬁed themselves as experienced gesture typists.

Original: However, none of them used the method dominantly, instead frequently switched between tap typing and gesture typing for text entry.
Revised: However, none of them used the method dominantly, instead frequently switched between tap typing and gesture typing for text entry.

Original: The remaining participants never or very rarely used gesture typing on their devices.
Revised: The remaining participants never or very rarely used gesture typing on their devices.

Original: Initially, we wanted to recruit more experienced gesture typists to compare the performance of inexperienced and experienced users to investigate whether the gesture typing skill acquired on mobile devices transferred to SwipeRing.
Revised: Initially, we wanted to recruit more experienced gesture typists to compare the performance of inexperienced and experienced users to investigate whether the gesture typing skill acquired on mobile devices transferred to SwipeRing.

Original: But we were unable to recruit experienced gesture typists after months of trying.
Revised: But we were unable to recruit experienced gesture typists after months of trying.

Original: This strengthens our argument that gesture typing is still not a dominant method of text entry, regardless of being much faster than tap typing [29], and using SwipeRing may encourage some users to apply the acquired skill on mobile devices.
Revised: This strengthens our argument that gesture typing is still not a dominant method of text entry, regardless of being much faster than tap typing [30], and using SwipeRing may encourage some users to apply the acquired skill on mobile devices.

Original: All participants received a small compensation for participating in the study.
Revised: All participants received a small compensation for participating in the study.

Original: 6.4 Performance Metrics
Revised: 6.4 Performance Metrics

Original: We calculated the conventional words per minute (WPM) [2] and total error rate (TER) performance metrics to measure the speed and accuracy of the keyboard, respectively.
Revised: We calculated the conventional words per minute (WPM) [2] and total error rate (TER) performance metrics to measure the speed and accuracy of the keyboard, respectively.

Original: TER [48] is a commonly used error metric in text entry research that measures the ratio of the total number of incorrect characters and corrected characters to the total number of correct , incorrect , and corrected characters in the transcribed text.
Revised: TER [51] is a commonly used error metric in text entry research that measures the ratio of the y = 9.1115x 0.2625 R² = 0. y = 6.6518x 0.2398 R² = 0. W o r d s P e r M i nu t e ( W

Original: We also calculated the actions per word metric that signiﬁes the average number of actions performed to enter one word.
Revised: P M ) Block SwipeRing C-Qᴡᴇʀᴛʏ Power (SwipeRing)

Original: {+Power (C-Qᴡᴇʀᴛʏ) 5 .2 .11.33 12.11 13.91 10.77 10.92 11.10 9.97 10.73 9.98 13.04 8.61 11.83 16.87 17.48 13.45 10.06 20.57 15.03 16.98 20.24 16.70 10.67 21.53 20.
+}
Revised: {+Power (C-Qᴡᴇʀᴛʏ) 5 .2 .11.33 12.11 13.91 10.77 10.92 11.10 9.97 10.73 9.98 13.04 8.61 11.83 16.87 17.48 13.45 10.06 20.57 15.03 16.98 20.24 16.70 10.67 21.53 20.
+}

Revised: [Figure]

Revised: [Figure]

Revised: [Figure]

Revised: total number of incorrect characters and corrected characters to the total number of correct , incorrect , and corrected characters in the transcribed text.

Revised: We also calculated the actions per word metric that signiﬁes the average number of actions performed to enter one word.

Original: An action could be a gesture performed to enter a word, a tap on the suggestion bar, or a gesture to delete an unwanted word or letter.
Revised: An action could be a gesture performed to enter a word, a tap on the suggestion bar, or a gesture to delete an unwanted word or letter.

Original: 6.5 Procedure
Revised: 6.5 Procedure

Original: The study was conducted in a quiet room, one participant at a time.
Revised: The study was conducted in a quiet room, one participant at a time.

Original: First, we introduced the keyboards to all participants, explained the study procedure, and collected their consents.
Revised: First, we introduced the keyboards to all participants, explained the study procedure, and collected their consents.

Original: We then asked them to complete a short demographics and mobile usage questionnaire.
Revised: We then asked them to complete a short demographics and mobile usage questionnaire.

Original: We instructed participants to sit on a chair, wear the smartwatch on their preferred hand, and practice with the keyboard they were assigned to by transcribing two short phrases.
Revised: We instructed participants to sit on a chair, wear the smartwatch on their preferred hand, and practice with the keyboard they were assigned to by transcribing two short phrases.

Original: These practice phrases were not included in the main study.
Revised: These practice phrases were not included in the main study.

Original: Interestingly, all participants decided to wear the smartwatch on their left hand and perform the gestures using the index ﬁnger of the other hand.
Revised: Interestingly, all participants decided to wear the smartwatch on their left hand and perform the gestures using the index ﬁnger of the other hand.

Original: The actual study started after that.
Revised: The actual study started after that.

Original: There were eight blocks in each condition, with at least 5-10 minutes gap between the blocks.
Revised: There were eight blocks in each condition, with at least 5-10 minutes gap between the blocks.

Original: In each block, participants transcribed ten random short English phrases from a set [33] using either C-Q WERTY Gesture or SwipeRing.
Revised: In each block, participants transcribed ten random short English phrases from a set [34] using either C-Q WERTY Gesture or SwipeRing.

Original: Both applications presented one phrase at a time at the bottom of the smartwatch (Fig. 9).
Revised: Both applications presented one phrase at a time at the bottom of the smartwatch (Fig. 9).

Original: Partic- ipants were instructed to read, understand, and memorize the phrase, transcribe it “as fast and accurate as possible” , then double-tap on the touchscreen to see the next phrase.
Revised: Partic- ipants were instructed to read, understand, and memorize the phrase, transcribe it “as fast and accurate as possible” , then double-tap on the touchscreen to see the next phrase.

Original: The transcribed text was displayed on the top of the smartwatch.
Revised: The transcribed text was displayed on the top of the smartwatch.

Original: Error correction was recommended but not forced.
Revised: Error correction was recommended but not forced.

Original: After the study, all participants completed a short post-study questionnaire that asked them to rate various aspects of the keyboard on a 7-point Likert scale.
Revised: After the study, all participants completed a short post-study questionnaire that asked them to rate various aspects of the keyboard on a 7-point Likert scale.

Original: It also enabled participants to comment and give feedback on the examined keyboards.
Revised: It also enabled participants to comment and give feedback on the examined keyboards.

Original: Due to the spread of COVID-19, the C-Q WERTY group partic- ipated in the study via Zoom, a teleconference application.
Revised: Due to the spread of COVID-19, the C-Q WERTY group partic- [Table] T o t a l E rr o r R a t e ( T E R ) Block

Revised: SwipeRing

Original: We personally delivered the smartwatch to each participant’s mailbox and scheduled individual online sessions with them.
Revised: C-Qᴡᴇʀᴛʏ Power (SwipeRing) Power (C-Qᴡᴇʀᴛʏ) 2. y = 2.1466x -0.126 R² = 0.

Revised: y = 2.7616x -0.074 R² = 0.

Revised: [Figure]

Revised: ipated in the study via Zoom, a teleconference application.

Revised: We personally delivered the smartwatch to each participant’s mailbox and scheduled individual online sessions with them.

Original: They were instructed to join the session from a quiet room.
Revised: They were instructed to join the session from a quiet room.

Original: All forms were completed and signed electronically.
Revised: All forms were completed and signed electronically.

Original: Apart from that, an online session followed the same structure as a physical session.
Revised: Apart from that, an online ses- sion followed the same structure as a physical session.

Original: A researcher observed and recorded a complete study session.
Revised: A researcher observed and recorded a complete study session.

Original: We piked up the devices after the study.
Revised: We picked up the devices after the study.

Original: The device, the charger, and the container were disinfected before delivery and after pickup.
Revised: The device, the charger, and the container were disinfected before delivery and after pickup.

Original: 6.6 Results
Revised: 6.6 Results

Original: A Shapiro-Wilk test revealed that the response variable residuals were normally distributed.
Revised: A Shapiro-Wilk test revealed that the response variable residuals were normally distributed.

Original: A Mauchly’s test indicated that the variances of populations were equal.
Revised: A Mauchly’s test indicated that the variances of populations were equal.

Original: Hence, we used a Mixed-design ANOVA for one between-subjects and one within-subjects factors (technique and block, respectively).
Revised: Hence, we used a Mixed-design ANOVA for one between-subjects and one within-subjects factors (technique and block, respectively).

Original: We used a Mann-Whitney U test to compare user ratings of various aspects of the two techniques.
Revised: We used a Mann-Whitney U test to compare user ratings of various aspects of the two techniques.

Original: 6.6.1 Entry Speed
Revised: 6.6.1 Entry Speed

Original: An ANOVA identiﬁed a signiﬁcant effect of technique on entry speed ( F 1 , 22 = 25 .
Revised: An ANOVA identiﬁed a signiﬁcant effect of technique on entry speed ( F 1 , 22 = 25 .

Original: p < . 0001).
Revised: p < . 0001).

Original: There was also a signiﬁcant effect of block ( F 7 , 22 = 63 .
Revised: There was also a signiﬁcant effect of block ( F 7 , 22 = 63 .

Original: 65 , p < . 0001).
Revised: 65 , p < . 0001).

Original: The technique × block interaction effect was also statistically signiﬁcant ( F 7 , 154 = 4 .
Revised: The technique × block interaction effect was also statistically signiﬁcant ( F 7 , 154 = 4 .

Original: 02 , p < . 0005).
Revised: 02 , p < . 0005).

Original: Fig.
Revised: Fig.

Original: 10 (top) illustrates average entry speed for both techniques in each block, ﬁtted to a function to model the power law of practice [4].
Revised: 10 (top) illustrates average entry speed for both techniques in each block, ﬁtted to a function to model the power law of practice [4].

Original: In the last block, the average entry speed with C-Q WERTY and SwipeRing were 11.20 WPM (SD = 3.0) and 16.67 WPM (SD = 5.36), respectively.
Revised: In the last block, the average entry speed with C-Q WERTY and SwipeRing were 11.20 WPM (SD = 3.0) and 16.67 WPM (SD = 5.36), respectively.

Original: Nine users of SwipeRing yielded a much higher entry speed than the maximum entry speed reached with
Revised: Nine users of SwipeRing yielded a much higher entry speed than the maximum entry speed reached with

Original: C-Q WERTY , illustrated in Fig.
Revised: C-Q WERTY , illustrated in Fig.

Original: 10 (bottom).
Revised: 10 (bottom).

Revised: The highest average

Revised: [Figure]

Revised: [Figure]

Revised: [Figure]

Original: The highest average entry speed in the last block was 21.53 WPM (P23, inexperienced gesture typist).
Revised: [Figure] W o r d s P e r M i nu t e ( W P M ) Block

Revised: [Figure]

Revised: entry speed in the last block was 21.53 WPM (P23, inexperienced gesture typist).

Original: 6.6.2 Error Rate
Revised: 6.6.2 Error Rate

Original: An ANOVA identiﬁed a signiﬁcant effect of technique on error rate ( F 1 , 22 = 24 .
Revised: An ANOVA identiﬁed a signiﬁcant effect of technique on error rate ( F 1 , 22 = 24 .

Original: 61 , p < . 0001).
Revised: 61 , p < . 0001).

Original: There was also a signiﬁcant effect of block ( F 7 , 22 = 2 . 89 , p < . 01).
Revised: There was also a signiﬁcant effect of block ( F 7 , 22 = 2 . 89 , p < . 01).

Original: However, the technique × block interaction effect was not signiﬁcant ( F 7 , 154 = 1 . 01 , p > . 05).
Revised: However, the technique × block interaction effect was not signiﬁcant ( F 7 , 154 = 1 . 01 , p > . 05).

Original: Fig.(top) illustrates average error rate for both techniques in each block, ﬁtted to a function to model the power law of practice [4].In the 9. [Figure] [Figure]
Revised: Fig.(top) illustrates average error rate for both techniques in each block, ﬁtted to a function to model the power law of practice [4].

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: y =

Original: [Figure]

Original: 1115x

Original: [Figure]

Original: y =

Original: [Figure]

Original: W o r d s

Original: P e r M i nu t e ( W

Original: P M )

Original: Block

Original: SwipeRing

Original: C-Qᴡᴇʀᴛʏ Power (SwipeRing)

Original: Power (C-Qᴡᴇʀᴛʏ)

Original: [Figure]

Original: [Figure]

Original: M ) Participant C-Qᴡᴇʀᴛʏ SwipeRing last block, the average error rates with C-Q WERTY and SwipeRing were 12.52% (SD = 13.91) and 5.56% (SD = 8.53), respectively.
Revised: In the last block, the average error rates with C-Q WERTY and SwipeRing were 12.52% (SD = 13.91) and 5.56% (SD = 8.53), respectively.

Original: 6.6.3 Actions per Word
Revised: 6.6.3 Actions per Word

Original: An ANOVA identiﬁed a signiﬁcant effect of technique on actions per word ( F 1 , 22 = 10 . 31 , p < . 005).
Revised: An ANOVA identiﬁed a signiﬁcant effect of technique on actions per word ( F 1 , 22 = 10 . 31 , p < . 005).

Original: There was also a signiﬁcant effect of block ( F 7 , 22 = 3 .
Revised: There was also a signiﬁcant effect of block ( F 7 , 22 = 3 .

Original: 14 , p < .
Revised: 14 , p < .

Original: However, the technique × block interaction effect was not signiﬁcant ( F 7 , 154 = 0 .
Revised: However, the technique × block interaction effect was not signiﬁcant ( F 7 , 154 = 0 .

Original: 61 , p > . 05).
Revised: 61 , p > . 05).

Original: Fig.(bottom) illustrates average actions per word for both techniques in each block, ﬁtted to a function to model the power law of practice [4].
Revised: Fig.(bottom) illustrates average actions per word for both techniques in each block, ﬁtted to a function to model the power law of practice [4].

Original: In the last block, the average actions per word with C-Q WERTY and SwipeRing were 2.45 (SD = 1.64) and 1.59 (SD = 0.72), respectively.
Revised: In the last block, the average actions per word with C-Q WERTY and SwipeRing were 2.45 (SD = 1.64) and 1.59 (SD = 0.72), respectively.

Original: 6.7 User Feedback
Revised: 6.7 Inexperienced vs. Experienced Gesture Typists

Revised: Although there were not enough data points to run statistical tests to compare the two user groups, average performance over blocks suggests that learning occurred with both experienced and inexpe- rienced gesture typists with both techniques. The average entry speed over block correlated well with the power law of practice for C-Q WERTY with both user groups (experienced: R 2 = 0 . 8142, inexperienced: R 2 = 0 . 9547), also for SwipeRing (experienced: R 2 = 0 . 8732, inexperienced: R 2 = 0 . 9702), illustrated in Fig.

Revised: The average error rate over block for both techniques, in contrast, correlated well with the power law of practice [4] for inexperienced participants (C-Q WERTY : R 2 = 0 . 86, SwipeRing: R 2 = 0 . 7019), but not for experienced participants (C-Q WERTY : R 2 = 0 . 0231,

Revised: SwipeRing: R 2 = 0 . 3329).

Revised: The average actions per word over block yielded a similar trend for C-Q WERTY , where learning was ob- served with inexperienced participants ( R 2 = 0 . 8978), but not with experienced participants ( R 2 = 0 . 1426).

Revised: However, with SwipeRing,

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: [Table]

Revised: [Figure]

Revised: both user groups continued improving with practice (experienced: R 2 = 0 .

Revised: 7171, inexperienced: R 2 = 0 . 8012), see Fig. 13.

Revised: 6.8 User Feedback

Original: A Mann-Whitney U test identiﬁed a signiﬁcant effect of technique on willingness to use ( U = 21 . 0 , Z = − 3 .
Revised: A Mann-Whitney U test identiﬁed a signiﬁcant effect of technique on willingness to use ( U = 21 . 0 , Z = − 3 .

Original: 1 , p < .
Revised: 1 , p < .

Original: perceived speed ( U = 22 . 5 , Z = − 3 .
Revised: perceived speed ( U = 22 . 5 , Z = − 3 .

Original: 07 , p < .
Revised: 07 , p < .

Original: 005), and perceived accuracy ( U = 27 . 
Revised: [Figure] and perceived accuracy ( U = 27 . 0 ,

Original: Z = − 2 .
Revised: Z = − 2 .

Original: 72 , p < . 01).
Revised: 72 , p < . 01).

Original: However, there was no signiﬁcant effect on ease of use ( U = 48 . 0 ,
Revised: However, there was no signiﬁcant effect on ease of use ( U = 48 . 0 ,

Original: Z = − 1 .
Revised: Z = − 1 .

Original: 5 , p > . 05) or learnability ( U = 66 .
Revised: 5 , p > . 05) or learnability ( U = 66 .

Original: Z = − 0 .
Revised: Z = − 0 .

Original: 37 , p > . 05).
Revised: 37 , p > . 05).

Original: Fig.
Revised: Fig.

Original: 12 illustrates median user ratings of all investigated aspects of the two keyboards on a 7-point Likert scale.
Revised: 14 illustrates median user ratings of all investigated aspects of the two keyboards on a 7-point Likert scale.

Original: 7 D ISCUSSION
Revised: 7 D ISCUSSION

Original: SwipeRing reached a competitive entry speed in only eight short blocks.
Revised: SwipeRing reached a competitive entry speed in only eight short blocks.

Original: It was 33% faster than C-Q WERTY .
Revised: It was 33% faster than C-Q WERTY .

Original: The average entry speed with C-Q WERTY and SwipeRing were 11.20 WPM and 16.67 WPM, respectively.
Revised: The average entry speed with C-Q WERTY and SwipeRing were 11.20 WPM and 16.67 WPM, respectively.

Original: Four participants reached over 20 WPM with
Revised: Four participants reached over 20 WPM with

Original: SwipeRing (Fig. 10, bottom).
Revised: SwipeRing (Fig. 10, bottom).

Original: Further, the SwipeRing group sur- passed the C-Q WERTY group’s maximum entry speed by the third block (Fig. 10, top).
Revised: Further, the SwipeRing group sur- passed the C-Q WERTY group’s maximum entry speed by the third Willingness to Use

Original: It also performed better than all popular circular text entry techniques (Table 2) and some Q WERTY -based techniques y = 7.6291x -0.177 R² = 0. y = 16.9x -0.169 R² = 0.
Revised: Ease of Use Learnability

Original: T o t a l E rr o r R a t e ( T E R ) Block SwipeRing
Revised: Perceived Speed Perceived Accuracy U s e r R a t i n g ( 7 - P o i n t

Original: C [Figure]
Revised: S c a l e )

Original: Qᴡᴇʀᴛʏ
Revised: C-Qᴡᴇʀᴛʏ

Original: Power (SwipeRing) Power (C-Qᴡᴇʀᴛʏ) 2.
Revised: SwipeRing block (Fig. 10, top).

Original: y = 2.1466x -0.126 R² = 0. y = 2.7616x -0.074 R² = 0. [Figure] (Table 1) for smartwatches.
Revised: It also performed better than all popular ring- shaped text entry techniques (Table 2) and some Q WERTY -based techniques (Table 1) for smartwatches.

Original: Yi et al.
Revised: Yi et al.

Original: [59] and WatchWriter [13] reported much higher entry speed than SwipeRing.
Revised: [62] and Watch- Writer [13] reported much higher entry speed than SwipeRing.

Original: Both techniques use aggressive statistical models with a miniature Q WERTY to ac- count for frequent incorrect target selection due to the smaller key sizes (the “fat-ﬁnger problem” [52]).
Revised: Both techniques use aggressive statistical models with a miniature Q WERTY to account for frequent incorrect target selection due to the smaller key sizes (the “fat-ﬁnger problem” [55]).

Original: This makes entering out-of- vocabulary words difﬁcult with these techniques.
Revised: This makes entering out-of-vocabulary words difﬁcult with these techniques.

Original: In fact, the former technique does not include a mechanism for entering out-of- vocabulary words [59, p. 58].
Revised: In fact, the former technique does not include a mechanism for entering outof-vocabulary words [62, p. 58].

Original: DualKey [15] and SwipeBoard [5] also reported higher entry speed than SwipeRing.
Revised: DualKey [16] and SwipeBoard [5] also reported higher entry speed than SwipeRing.

Original: However, DualKey de- pends on external hardware to distinguish between different ﬁngers and has a steep learning curve (the reported entry speed was achieved in the 15th session).
Revised: However, DualKey depends on external hardware to distinguish between different ﬁngers and has a steep learning curve (the reported entry speed was achieved in the 15th session).

Original: SwipeBoard, on the other hand, was evaluated on a tablet computer, hence unclear whether the reported entry speed can be maintained on an actual smartwatch.
Revised: SwipeBoard, on the other hand, was evaluated on a tablet computer, hence unclear whether the reported entry speed can be maintained on an actual smartwatch.

Original: Besides, all of these keyboards occupy about 45–85% of the screen real-estate, leaving a little room for displaying the entered text, let alone additional information.
Revised: Be- sides, all of these keyboards occupy about 45–85% of the screen real-estate, leaving a little room for displaying the entered text, let alone additional information.

Original: There was a signiﬁcant effect of block and technique × block on entry speed.
Revised: There was a signiﬁcant effect of block and technique × block on entry speed.

Original: Entry speed increased by 38% with C-Q WERTY and 43% with and SwipeRing in the last block compared to the ﬁrst.
Revised: Entry speed increased by 38% with C-Q WERTY and 43% with and SwipeRing in the last block compared to the ﬁrst.

Original: The average entry speed over block for both techniques correlated well with the power law of practice [4] ( R 2 = 0 . 9588).
Revised: The average entry speed over block for both techniques correlated well with the power law of practice [4] ( R 2 = 0 . 9588).

Original: However, the learning curve for C-Q WERTY was ﬂattening out by the last block, while SwipeRing was going strong.
Revised: However, the learning curve for C-Q WERTY was ﬂattening out by the last block, while SwipeRing was going strong.

Original: Analysis revealed that entry speed improved by 2% with C-Q WERTY and 13% with SwipeRing in the last block compared to the second-last.
Revised: An analysis revealed that entry speed improved by 2% with C-Q WERTY and 13% with SwipeRing in the last block compared to the second- last.

Original: This suggests that SwipeRing did not reach its highest possible speed in the study.
Revised: This suggests that SwipeRing did not reach its highest possible speed in the study.

Original: Rel- evantly, the highest entry speed recorded in the study was 33. WPM (P23, Block 6).
Revised: Relevantly, the highest entry speed recorded in the study was 33.18 WPM (P23, Block 6).

Original: There was a signiﬁcant effect of technique on error rate.
Revised: There was a signiﬁcant effect of technique on error rate.

Original: SwipeRing was signiﬁcantly more accurate than C-Q WERTY (Fig. 11, top).The average error rate with C-Q WERTY and [Figure] to “7”
Revised: SwipeRing was signiﬁcantly more accurate than C-Q WERTY (Fig. 11, top).

Original: “Strongly Disagree” to “Strongly Agree”.

Original: The  error bars ± standard deviations SwipeRing were 12.52% and 5.56%, respectively, in the last block (56% fewer errors with SwipeRing).
Revised: The average error rate with C-Q WERTY and SwipeRing were 12.52% and 5.56%, respectively, in the last block (56% fewer errors with SwipeRing).

Original: This is unsurprising since the designers of C-Q WERTY also reported a high error rate with the technique (20.6%) using the same TER metric [7] that accounts for both corrected and uncorrected errors in the transcribed text [48].
Revised: This is unsurprising since the designers of C-Q WERTY also reported a high error rate with the technique (20.6%) using the same TER metric [7] that accounts for both corrected and uncorrected errors in the transcribed text [51].

Original: Most text entry techniques for smartwatches report character error rate (CER) that only accounts for the uncorrected errors in the transcribed text [2].
Revised: Most text entry techniques for smartwatches report character er- ror rate (CER) that only accounts for the uncorrected errors in the transcribed text [2].

Original: Most errors with C-Q WERTY were committed due to incorrect target selection since the keys were too small.
Revised: Most errors with C-Q WERTY were commit- ted due to incorrect target selection since the keys were too small.

Original: SwipeRing yielded a lower error rate due to the larger zones that were designed to accommodate precise target selection.
Revised: SwipeRing yielded a lower error rate due to the larger zones that were designed to accommodate precise target selection.

Original: There was a signiﬁcant effect of block on error rate.
Revised: There was a signiﬁcant effect of block on error rate.

Original: Participants committed 13% fewer errors with C-Q WERTY and 29% fewer errors with SwipeRing in the last block compared to the ﬁrst.
Revised: Participants committed 13% fewer errors with C-Q WERTY and 29% fewer errors with SwipeRing in the last block compared to the ﬁrst.

Original: The average error rate over block correlated moderately for C-Q WERTY ( R 2 = 0 . 5895) but well for SwipeRing ( R 2 = 0 . 8706) with the power law of practice [4].
Revised: The average error rate over block correlated moderately for C-Q WERTY ( R 2 = 0 . 5895) but well for SwipeRing ( R 2 = 0 . 8706) with the power law of practice [4].

Original: Hence, it is likely that SwipeRing will become much more accurate with practice.
Revised: Hence, it is likely that SwipeRing will become much more accurate with practice.

Original: Actions per word yielded a similar pattern as error rate.
Revised: Actions per word yielded a similar pattern as error rate.

Original: SwipeRing consistently required fewer actions to enter words than C-Q WERTY (Fig. 11, bottom).
Revised: SwipeRing consistently required fewer actions to enter words than C-Q WERTY (Fig. 11, bottom).

Original: C-Q WERTY and
Revised: C-Q WERTY and

Original: SwipeRing required on average 2.45 and 1.59 actions per word in the last block, respectively (35% fewer actions with SwipeRing).
Revised: SwipeRing required on average 2.45 and 1.59 actions per word in the last block, respectively (35% fewer actions with SwipeRing).

Original: This is mainly because participants performed fewer corrective actions with SwipeRing than C-Q WERTY .
Revised: This is mainly because participants performed fewer corrective actions with SwipeRing than C-Q WERTY .

Original: There was also a signiﬁcant effect of block.
Revised: There was also a signiﬁcant effect of block.

Original: The average actions per word over block correlated well for SwipeRing ( R 2 = 0 . 8459) but not for C-Q WERTY ( R 2 = 0 . 4999) with the power law of practice [4].
Revised: The average actions per word over block correlated well for SwipeRing ( R 2 = 0 . 8459) but not for C-Q WERTY ( R 2 = 0 . 4999) with the power law of practice [4].

Original: This suggests that actions per word with SwipeRing is likely to further improve with practice.
Revised: This suggests that actions per word with SwipeRing is likely to further improve with practice.

Original: Qualitative results revealed that the SwipeRing group found the examined technique faster and more accurate than the C-Q WERTY group (Fig. 12).
Revised: Qualitative results revealed that the SwipeRing group found the examined technique faster and more accurate than the C-Q WERTY group (Fig. 14).

Original: These differences were statistically signiﬁcant.
Revised: These differences were statistically signiﬁcant.

Original: Con- sequently, the SwipeRing group was signiﬁcantly more interested in using the technique on their devices than the C-Q WERTY group.
Revised: Con- sequently, the SwipeRing group was signiﬁcantly more interested in using the technique on their devices than the C-Q WERTY group.

Original: However, both techniques were rated comparably on ease of use and learnability, which is unsurprising since both techniques used similar layouts.
Revised: However, both techniques were rated comparably on ease of use and learnability, which is unsurprising since both techniques used similar layouts.

Original: We compared the performance of C-Q WERTY in our study with the results from the literature to ﬁnd out whether conducting the study remotely affected its performance.
Revised: We compared the performance of C-Q WERTY in our study with the results from the literature to ﬁnd out whether conducting the study remotely affected its performance.

Original: Costagliola et al.
Revised: Costagliola et al.

Original: [7] re- ported a 7.7 WPM entry speed with a 20.6% error rate on a slightly larger smartwatch using the same phrase set in a single block con- taining 6 phrases.
Revised: [7] re- ported a 7.7 WPM entry speed with a 20.6% error rate on a slightly larger smartwatch using the same phrase set in a single block con- taining 6 phrases.

Original: In our study, C-Q WERTY yielded a comparable
Revised: In our study, C-Q WERTY yielded a comparable

Original: WPM and 16.8% error rate in the ﬁrst block containing 10 phrases.
Revised: WPM and 16.8% error rate in the ﬁrst block containing 10 phrases.

Original: [Figure]

Original: W o r d s

Original: P e r M i nu t e ( W P M )

Original: [Figure]

Original: 7.1 Skill Transfer from Virtual Q WERTY
Revised: 7.1 Skill Transfer from Virtual Q WERTY

Original: Learning occurred with both experienced and inexperienced ges- ture typists with both techniques.

Original: The average entry speed over block correlated well with the power law of practice for C-Q WERTY with both user groups (experienced: R 2 = 0 . 8142, inexperienced:

Original: R 2 = 0 . 9547), also for SwipeRing (experienced: R 2 = 0 .

Original: 8732, inexperienced: R 2 = 0 . 9702).

Original: Although there were not enough data points to run statistical tests, average performance over blocks sug- gests that experienced participants were performing much better with both techniques from the start.
Revised: Although there were not enough data points to run statistical tests, average performance over blocks suggests that experienced participants were performing much better with both techniques from the start.

Original: Fig.
Revised: Fig.

Original: 13 shows that experienced partic- ipants consistently performed better than inexperienced participants.
Revised: 12 shows that experienced participants consistently performed better than inexperienced participants.

Original: This suggests that experienced participants were able to transfer their smartphone gesture typing skills to both techniques.
Revised: This suggests that experienced participants were able to transfer their smartphone gesture typing skills to both techniques.

Original: However, with C-Q WERTY , inexperienced participants almost caught up with the experienced participants by the last block.
Revised: However, with C-Q WERTY , inexperienced participants almost caught up with the experienced participants by the last block.

Original: While with SwipeRing, both user groups were learning at comparable rates in all blocks.
Revised: While with SwipeRing, both user groups were learning at comparable rates in all blocks.

Original: Besides, both experienced and inexperienced participants constantly performed better with SwipeRing than C-Q WERTY .
Revised: Besides, both experienced and inexperienced participants constantly per- formed better with SwipeRing than C-Q WERTY .

Original: These indicate towards the possibility that optimizing the zones for gesture similarities facilitated a higher rate of skill transfer.
Revised: These indicate towards the possibility that optimizing the zones for gesture similar- ities facilitated a higher rate of skill transfer.

Original: As blocks progressed, experienced participants were most probably more conﬁdent, consciously or subconsciously, in applying their gesture typing skills to SwipeRing.
Revised: As blocks progressed, experienced participants were most probably more conﬁdent, con- sciously or subconsciously, in applying their gesture typing skills to SwipeRing.

Original: Interestingly, error rate and actions per word patterns were quite different than the patterns observed in entry speed.
Revised: Interestingly, error rate and actions per word patterns were quite different from the patterns observed in entry speed.

Original: With SwipeRing, experienced participants were consistently better than inexperienced users, while inexperienced participants were learning to be more accurate.
Revised: With SwipeRing, experienced participants were consistently better than inexperienced users, while inexperienced participants were learning to be more accurate.

Original: The average error rate over block correlated well with the power law of practice [4] for inexperienced participants ( R 2 = 0 . 7019) but not for experienced participants ( R 2 = 0 . 3329).

Original: We speculate this is because experienced participants made fewer errors than inexperienced participants, which required performing fewer corrective actions (a phenomenon reported in the literature [3]).
Revised: We speculate this is because experienced participants made fewer errors than inexperienced participants, which required performing fewer corrective actions (a phenomenon reported in the literature [3]).

Original: In contrast, with C-Q WERTY , experienced participants committed more errors, requiring more corrective actions.
Revised: In contrast, with C-Q WERTY , experienced participants committed more errors, requiring more corrective actions.

Original: In Fig. 14, one can see that experienced participants’ error rates and actions per word went up and down in alternating blocks.
Revised: In Fig. 13, one can see that experienced participants’ error rates and actions per word went up and down in alternating blocks.

Original: We do not have a deﬁnite explanation for this, but based on user comments we speculate that this is because experienced participants were trying to apply their gesture typing skills to C-Q WERTY , only committing more errors [Table] [Figure] A c t i o n s P e r
Revised: We do not have a deﬁnite explanation for this, but based on user comments we speculate that this is because experienced participants were trying to apply their gesture typing skills to C-Q WERTY , only committing more errors due to the smaller target size, then reduced speed in the following block to increase accuracy (i.e., the speed-accuracy trade-off).

Original: W o r d (

Original: A P W )

Original: Block

Original: [Figure]

Original: due to the smaller target size, then reduced speed in the following block to increase accuracy.

Original: This process continued till the end of the study.
Revised: This process continued till the end of the study.

Original: In contrast, actions per wordcontinued improving for both experienced ( R 2 = 0 . 7171) and inexperienced ( R 2 = 0 . 8012) participants with SwipeRing.
Revised: Interest- ingly, with SwipeRing, both inexperienced and experienced gesture typists were improving their average actions per word with practice.

Revised: It could be because participants gradually learned how to exploit the wider zones of the layout to reduce the number of incorrect actions.

Original: This suggests that optimizing the zones for precise target selection facilitated a higher rate of skill acquisition.
Revised: This suggests that optimizing the zones for precise target selection facilitated a higher rate of skill acquisition.

Original: 8 C ONCLUSION
Revised: 8 C ONCLUSION

Original: We presented SwipeRing, a circular keyboard arranged around the bezel of a smartwatch to enable gesture typing with the support of a statistical decoder.
Revised: We presented SwipeRing, a ring-shaped keyboard arranged around the bezel of a smartwatch to enable gesture typing with the support of a statistical decoder.

Original: It also enables character-based text en- try by using a multi-tap like approach.
Revised: It also enables character-based text entry by using a multi-tap like approach.

Original: It divides the layout into seven zones and maintains a resemblance to the standard Q WERTY layout.
Revised: It divides the layout into seven zones and maintains a resemblance to the standard Q WERTY layout.

Original: Unlike most existing solutions, it does not occupy most of the touchscreen real-estate or require repeated actions to enter most words.
Revised: Unlike most existing solutions, it does not occupy most of the touchscreen real-estate or require repeated actions to enter most words.

Original: Yet, it employs the whole screen for drawing gestures, which is more comfortable than drawing gestures on a miniature Q WERTY .
Revised: Yet, it employs the whole screen for drawing gestures, which is more comfortable than drawing gestures on a miniature Q WERTY .

Original: The keyboard is optimized for target selection and to maintain similarities between the gestures drawn on a smartwatch and a virtual Q WERTY to facilitate skill transfer between devices.
Revised: The keyboard is optimized for target selection and to maintain similarities between the gestures drawn on a smartwatch and a virtual Q WERTY to facilitate skill transfer between devices.

Original: We compared the technique with C-Q WERTY , a similar technique that uses almost the same layout to enable gesture typing, but do not divide the keyboard into zones or optimize the zones for target selec- tion and gesture similarity.
Revised: We compared the technique with C-Q WERTY , a similar technique that uses almost the same layout to enable gesture typing, but does not divide the keyboard into zones or optimize the zones for target selection and gesture similarity.

Original: In the study, SwipeRing yielded a 33% higher entry speed, 56% lower error rate, and 35% lower actions per word than C-Q WERTY in the last block.
Revised: In the study, SwipeRing yielded a 33% higher entry speed, 56% lower error rate, and 35% lower actions per word than C-Q WERTY in the last block.

Original: The average entry speed with SwipeRing was 16.67 WPM, faster than all popular circular and most Q WERTY -based text entry techniques for smartwatches.
Revised: The average entry speed with SwipeRing was 16.67 WPM, faster than all popular ring-shaped and most Q WERTY -based text entry techniques for smartwatches.

Original: Results indicate towards the possibility that skilled gesture typists were able to transfer their skills to SwipeRing.
Revised: Results indicate towards the possibility that skilled gesture typists were able to transfer their skills to SwipeRing.

Original: Besides, participants found the keyboard easy to learn, easy to use, fast, and accurate, thus wanted to continue using it on smartwatches.
Revised: Besides, participants found the keyboard easy to learn, easy to use, fast, and accurate, thus wanted to continue using it on smartwatches.

Original: 8.1 Future Work
Revised: 9 F UTURE W ORK

Revised: There are multiple possible extensions to our work.

Original: The proposed keyboard could be useful in saving touchscreen real- estate on larger devices, such as smartphones and tablets.
Revised: First, the pro- posed keyboard could be useful in saving touchscreen real-estate on larger devices, such as smartphones and tablets.

Original: The keyboard could appear on the screen like a ﬂoating widget, where users perform gestures to enter text.
Revised: The keyboard could appear on the screen like a ﬂoating widget, where users perform gestures to enter text.

Original: We will also explore this possibility.

Original: We will explore SwipeRing  in virtual and augmented reality by using a smartwatch or different types of controllers.
Revised: Second, the SwipeRing can be used in virtual and augmented reality by using a smartwatch or different types of controllers.

Original: Finally, we will investigate the possibility of  eyes-free text entry with SwipeRing.
Revised: Finally, there is a possibility of an eyes-free text entry with SwipeRing.

Original: We speculate, when the positions of the zones are learned, users would be able to perform the gestures without visual aid.
Revised: We speculate, when the positions of the zones are learned, users would be able to perform the gestures without visual aid.

Original: This can make the whole touchscreen available to display additional information by making the keyboard invisible.
Revised: This can make the whole touchscreen available to display additional information by making the keyboard invisible.

Revised: 9.1 Limitations

Revised: We discussed several limitations of the work.

Revised: To summarize: ﬁrst, we had different numbers of experienced and inexperienced participants in the study.

Revised: The sample size was also small.

Revised: Hence, a deﬁnitive conclusion cannot be drawn about the transference of skill between devices.

Revised: Second, due to the spread of COVID-19, we had to switch to an online format mid-study, as in-person studies were unsafe.

Revised: This resulted in one condition being studied in-person, while the other online.

Revised: However, based on the performance reported in prior work, we speculate that this did not impact the ﬁndings of this research.

Revised: Finally, we did not investigate the proposed method in mobile settings, such as while walking or commuting.

Revised: However, we anticipate our method to perform much better in such scenarios compared to the conventional methods since it does not always require precise target selection.

Revised: A CKNOWLEDGMENTS

Revised: We thank Costagliola, Rosa, D’Arco, Gregorio, Fuccella, and Lupo for providing us with the original source code of C-Q WERTY [7].

Revised: This project was supported in part by a Senate Faculty Award and a Graduate Student Opportunity Program (GSOP) Fellowship at UC

Revised: Merced.