JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump
Romain Nith

Jacob Serfaty

University of Chicagornith@uchicago.edu

Samuel G Shatzkin

University of Chicago
jserfaty@uchicago.edu

Alan Shen

University of Chicago
sshatzkin@uchicago.edu

Pedro Lopes

University of Chicago
alans@uchicago.edu

University of Chicago
pedrolopes@uchicago.edu

Figure 1: (a) We propose JumpMod, a wearable device that modifies the user’s perceived jump. It achieves this by moving aweight along the user’s back, causing an inertial force that modifies one’s sense of vertical momentum. We found, in our seconduser study, that JumpMod creates five distinct sensations: a sense of (b) jumping higher; (c) being pulled lower; (d) landing
harder or (e) softer; and (f) being pulled higher—demonstrated in our applications, including VR and interactive sports such as(f) interactive basketball.

ABSTRACT
Vertical force-feedback is extremely rare in mainstream interactive
experiences. This happens because existing haptic devices capableof sufficiently strong forces that would modify a user’s jump require
grounding (e.g., motion platforms or pulleys) or cumbersome actuators (e.g., large propellers attached or held by the user). To enableinteractive experiences to feature jump-based haptics without sacrificing wearability, we propose JumpMod, an untethered backpackthat modifies one’s sense of jumping. JumpMod achieves this bymoving a weight up/down along the user’s back, which modifies
perceived jump momentum—creating accelerated & decelerated
jump sensations. In our second study, we empirically found that
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https://doi.org/10.1145/3544548.3580764

our device can render five effects: jump higher, land harder/softer,
pulled higher/lower. Based on these, we designed four jumping
experiences for VR & sports. Finally, in our third study, we foundthat participants preferred wearing our device in an interactive
context, such as one of our jump-based VR applications.

KEYWORDS
jumping, full-body, haptics, virtual reality, wearable, backpack
ACM Reference Format:
Romain Nith, Jacob Serfaty, Samuel G Shatzkin, Alan Shen, and Pedro Lopes.
2023. JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump. In
Proceedings of the 2023 CHI Conference on Human Factors in Computing
Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York,NY, USA, 15 pages. https://doi.org/10.1145/3544548.3580764

1

INTRODUCTION

A decade ago, users saw a fundamental change in the way theyinteracted with digital experiences in their homes. With the introduction of motion sensing (e.g., Kinect), the user’s full body could
now serve as an input, enabling a more bodily way to interface
with computers. Today, we witness many interactive experiences

CHI ’23, April 23–28, 2023, Hamburg, Germany

taking advantage of this, from interactive sports (e.g., laser tag orinteractive bouldering) to physical VR experiences (e.g., BeatSaberor VR fitness).
However, enabling interactive devices to move the user’s entire
body (i.e., body-scale force-feedback) has proven challenging. Thisis primarily due to the large forces required to move the user’s bodymass. Moreover, it is arguably easier to achieve force-feedback in
the horizontal plane since a haptic device needs only to overcome
the inertia and friction, which might explain the wider proliferationof interactive treadmills [9, 19, 42, 50] or walkable floors [20, 48]
when compared to vertical force-feedback devices. A vertical forcefeedback device needs to, additionally, overcome gravity (i.e., pushagainst the user’s whole-body weight).
When researchers and practitioners (e.g., theme-parks or VR
arcades designers) need to vertically actuate the user’s entire body,they usually resort to installing large actuators in the environment,such as motion platforms [3, 4, 11, 45], mechanically-actuated pulleys [10, 12, 23, 27, 57, 58], wind tunnels [6, 28, 35] or attaching
powerful propellers to the user’s body [47, 52]. While these are effective since their output force is large, they completely limit users’
mobility—these devices are at odds with users’ home setups (e.g.,
free-walking VR experiences [17, 33, 44]) and are at odds with experiences where users are highly mobile (e.g., interactive sports[1, 5, 29, 36, 40]). Instead, to enable more mobile contexts, a verticalbody-scale actuator that is untethered and wearable would proveideal.
To tackle this and expand the portability of vertical forcefeedback, we propose JumpMod, an untethered backpack that modifies the user’s perceived jump. Our device, depicted in Figure 1,
achieves vertical effects by moving a weight along the user’s back,even while in mid-air. The key behind JumpMod is that its actuator does not physically lift the user—as this would require motors
and infrastructure 10x larger/heavier, preventing our small formfactor—instead, JumpMod creates only a sufficiently strong inertial
force that users can feel, which modifies their perceived jump—thisforce generated by our device is above Weber’s fraction for thenoticeable perception of a force [13] (refer to Study#2 and Technical
Evaluation for details).

Romain Nith et al.

As such, interactive experiences can make use of JumpMod at
different phases of a user’s jump (i.e., launch, ascension, descension,
landing) to create five distinct effects. We confirmed these findings
via three user studies: (1) We found an accuracy of 94% in ourjump detection, while participants were not instructed to jump inany controlled manner (i.e., they jumped as they saw fit, including
jumps designed to trigger false positives). (2) We empirically foundthat JumpMod evoked five unique jump effects: jump higher, land
harder, land softer, pulled higher, and pulled lower—to robustly
measure that these effects were created by our device’s moving
weight, we conducted this study in the absence of any visual orsound stimuli and included a spoofing condition, with additional
vibrations and weak motor movements. (3) We found that JumpMod
added realism to VR jump sensations, even compared to existing
VR techniques that visually exaggerate the avatar’s height [17, 56].
Finally, to demonstrate the applicability of our device, we designed
four experiences that utilize these jump effects—two interactive
sports and two VR-based applications.

2

WALKTHROUGH

To illustrate the applicability of our device, we demonstrate it withan example of a jumping-based experience in VR. The haptic effects
for this experience were designed based on empirical results fromour perceptual experiment (see User Study#2). In this walkthrough,our VR user is wearing our untethered backpack and a wireless VR
headset. This VR experience is set in a farm scene shown in Figure
2 (b) and the game is played by running and jumping in place to
avoid obstacles and collect points by smashing pumpkins.
Jumping higher. Our user is running in place and attempting
to jump over obstacles, however, when they attempt to jump overa cow, they fail—they lose speed and must try again. On the second
attempt, depicted in Figure 2 (a), they collect a jump boost powerup. As the user jumps again, our device actuates its weight up insync with the user’s launch from the ground (refer to Technical
Evaluation for details; in short, our device takes only 20ms to detect
any jump phase with high accuracy). This extra inertial force fromthe moving weight creates a sense of jumping higher (see User
Study#2 for the validation of all perceived effects). As a result, notFigure 2: (a) JumpMod creates vertical haptic feedback by physically moving a weight up/down. It can create (b) the experienceof jumping higher (during a power-up) by moving the weight up in sync with the launch phase of a jump as depicted by theplot.

JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

CHI ’23, April 23–28, 2023, Hamburg, Germany

only do they see their avatar jumping over the cow (as typical injumping-based VR experiences, we also scale up the avatar’s jump),
they feel this matches the physical sensation of jumping higher—in
fact, in our third study, we found that the combination of our device
with the visual exaggerated jump was more realistic than only the
traditional visual jump effects.
Pulling down. Figure 3 (a) illustrates our user jumping over the
next obstacle, a haystack. This time when the user is still mid-jump
and about to land, the wind blows harder and pulls the user down(the user sees and hears the wind effects too). To generate this“pulling” sensation, our device moves its weight down as the user
is landing.

Figure 4: (a) Landing harder is rendered when the user lands
on a pumpkin to smash it. (b) The weight slowly moves up to
reset its position in preparation for the next effect. (c). Pulled
higher is rendered when the user is assisted by a bird during
a jump.

Figure 3: (a) Pulled down is rendered when the wind is blowing the user. (b). Land softer is rendered when the user landsin the mud.
Landing softer. Figure 3 (b) also illustrates our user landing on
mud rather than on solid ground as before. To render this “softer”
landing from the mud, our device moves its weight up when theuser is landing.
Landing harder. Next, our user spots a pumpkin in Figure 4 (a),which they need to smash for points. They collect a force powerup that enables smashing pumpkins. To render this sensation of“landing harder” on the pumpkin, our device moves the weight
down when the user is descending (past the apex, the highest
point of their jump).
Resetting the weight’s position. So far, our VR experience wasdesigned with all effects in a sequence that left the device’s weightin the place where the next effect was required. But this is notnecessary; there are several ways that a designer can use to reset

the weight’s position, ranging from options that conserve realism
to those that provide speed: (1) planning the order of the effects so
no reset is needed, (2) slowly driving the weight so that user cannot
perceive it, (3) associating the reset with other in-game events(e.g., an earthquake that triggers back-and-forth vibrations, but also
move the weight to the intended position), and (4) moving it rapidly
before the next event. In this particular experience, the designerchose to look ahead to the next obstacle and, if needed, always
slowly reset the weight to the required start position (inspired by
HapticTurk’s lookahead for the next haptic events [8]). As the next
event requires the weight to start at the top but is currently at thebottom, the VR application sends a “5s reset up” instruction to ourdevice, which brings the weight up in 5s. To make sure the weightis at the correct position, our device implements a positional PID
controller (see Implementation details).
Pulled higher: Finally, Figure 4 (c) depicts the user having
collected all the pumpkins and is about to jump over a final obstacle—
they can already see the finish line. As they jump, a bird flies next
to the user and pulls the user up to help them to the finish line. Our
device renders this “pulled higher” by moving the weight down at
the user’s launch.

CHI ’23, April 23–28, 2023, Hamburg, Germany

3

RELATED WORK

Our work builds on body-scale haptics devices that render verticalforce-feedback, i.e., devices capable of actuating the user’s body
upwards or downwards. As such, we take inspiration from groundedhaptic devices that explored these effects, but also, from novel
approaches that modulate the user’s perception to create haptic
effects at portable form factors.

3.1

Body-scale grounded haptics devices that
render vertical force-feedback

The canonical way to realize very large forces, such as those capableof actuating the user’s body, is to push from the ground against theuser’s body—as such, these types of haptic devices (which typically
employ motorized pulleys or arms) are denoted as grounded devices.
These devices excel in the amount of output force and are arguably
some of the most mainstream haptic devices as they are featured
prominently in theme parks, amusement rides, and selected theaters
equipped with motion seats.
Vertical motion platforms. The most popular way to realize
vertical haptic effects is to physically actuate a platform in which
the user is standing or sitting in (typically with strong electrical,
hydraulic, or pneumatic actuators). For instance, the Stewart motion platform generates accelerations (typically in all directions,
including vertically), while the user is sitting [3, 4, 11, 34] or laying
down [45]. Other variations of this actuation mechanism include
stepping on individual platforms (one for each foot, to simulate
walking on various terrain heights) [21, 49], walking on platforms
that change the user’s height [20, 48], or even combining a staircase
and motorized lift to create infinite stairs in VR [7].
Pulley-based vertical actuation. Another method to rendervertical haptic effects is to pull the user’s body weight via pulleys/cables. For instance, Pull-Ups [58] can pull/push the user’s
body to the ceiling by altering the length of a pulley that the user
is grabbing onto. Instead of requiring users to grasp, other devices
suspend the user’s body in midair with pulleys to simulate the skydiving [12] by catching the user with cables and counterweights
after jumping off a platform. By motorizing the pulleys, the senseof gravity can also be manipulated with the help of closed-loopsystems where jumping physics can be altered such as jumping on
the moon [23, 27], or zero-gravity to train astronauts [10].
Replacing actuators with passive props or humanactuators. Two more recent alternative approaches to realizing
vertical haptic effects include: (1) replacing mechanical actuatorswith “human actuators” [8], and (2) replacing actuators with passive props, such as trampolines [54] or physical stair props [38, 39].
While these are promising ways to circumvent some of the limitations of large grounded vertical actuators, these do not inherit thesame interactive properties as the latter, i.e., fast response speed,
force, and, most importantly, most of these are strictly bounded tothe use of virtual reality to not break the immersion.
Limitations. Thus, typical vertical actuation is still based on motion platforms and pulleys. However, these devices are comprisedof large actuators, which consume a lot of energy and are grounded
to the infrastructure. As a result, existing devices capable of verticaleffects are not compatible with mobile experiences (e.g., sports or
free-walking VR).

Romain Nith et al.

3.2

Body-scale Ungrounded haptics devices that
render vertical force-feedback

To tackle the limitations of grounded vertical force-feedback, researchers explored alternative wearable actuators.
Changing center of gravity. One way to approximate an ungrounded rendition of the steward platform is to move weightsthat shift the user’s center of gravity. Maekawa et al. proposed awearable robotic tail that swings as a counterweight, preventing
the user from falling [32]. Similarly, Arque [37] uses a robotic tailmechanism to also provide haptic feedback for forces in VR. Inclination Manipulator [51] creates the sensation of inclination in
VR by moving a weight horizontally away from the body. These
devices mostly render inclination, not vertical momentum. On theother hand, ergonomic backpacks [18, 46] help reduce strain fromheavy loads by reducing vertical momentum but do not provide
force-feedback.
Actuating with Air. One way to actuate the user’s body vertically without the need for grounding the user to an actuator is byejecting air from a device worn by the user [25, 52] or from a device
that the user is grasping [47]. For instance, Augmented jump [52]attached sixteen ducted fans to the user via a frame. By powering
all fans, the device is able to lift 50kg of weight off the ground,simulating a 75% reduced gravity when the user jumps. However,
because the device requires a lot of power, users need to carry 24kg
of batteries in addition to the user and the hardware’s weight andits fans produce a lot of noise. Similarly, Weighted Walking [25]attaches two ducted fans per leg to emulate walking on different
viscosity or simulate different gravity (each fan produces 22.4N, butalso a lot of noise). Another approach, Virtual Super-Leaping [47],is a handheld device with eight mounted propellers. By jumpingwith the device held in their hands, users experience force applied totheir hands, while in mid-air. While the force is not applied directly
to the torso but to the hands, by coupling this effect with VR visuals,
users can perceive bigger jumps when the propellers are thrustingup (27.8N, not enough to lift the user as opposed to Augmentedjump). This device only pushes air in one direction and, as such,it is limited in sensations (e.g., jumping higher, gliding, or landing
harder by turning off the propellers). Being a propeller-based actuator, the device inevitably produces a lot of noise—measured at
95.3dB.
Limitations. To sum up, while these techniques are promising,
they have four key limitations. First, these devices demonstrate
only how to increase the user’s jump, i.e., jumping higher, but missadding more expressivity in vertical effects, such as modifying one’s
landing to feel both harder or softer, or even rendering a sense ofbeing pulled up/down in the middle of a jump. Second, propellerbased actuators require tremendous amounts of power, making
these actuators only available for short experiences. Third, some ofthese propeller approaches (e.g., Virtual Super-Leaping [47]) require
users to hold onto the propellers, which prevents their hands frombeing used in the interactive experience. Finally, propellers comewith additional limitations, such as loudness and ejecting air tothe body/face, which creates unwanted haptics. Instead, we takeinspiration from these approaches but produce jump sensations
using a new type of actuator: our backpack moves a weight alongJumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

CHI ’23, April 23–28, 2023, Hamburg, Germany

Figure 5: (a) JumpMod is untethered and wearable; it features: a custom battery that doubles as a 2kg weight, a driving belt
system with an encoder, and a microcontroller with Bluetooth. (b) Four anchor points and a sternum strap secure the backpack
to the user.
the user’s back, which we found to produce new jump sensations
at a wearable form factor.

3.3

Modifying virtual jumps using VR illusions

Finally, one alternative approach that is only applicable to VR experiences is to manipulate the virtual representation of the user’s jump
to modify their sense of jump – this is often denoted as Redirected
Jumping. In these VR techniques, the avatar’s jump trajectory ischanged from the user’s actual jump. For example, PseudoJumpOn[43] exaggerates the virtual jumping trajectory and allows users
to feel as if they jumped higher. To make this illusion believable,
many researchers have conducted extensive studies to evaluate
the virtual jump trajectories [16, 17, 22, 30, 31] to optimize thephysical space for jumping in VR [16]. Malleable embodiment [24]uses another approach; it visually depicts the user’s past or (predicted) future movements to induce a sensation of jumping heavier
or lighter. However, this method not only works solely for VR; italso demands that user always sees a reflected view of their avatar(using VR mirrors, and so forth).
While these techniques are useful in VR, they are not applicablebeyond virtual interactions. Moreover, as we will demonstrate in
our third user study, even in the case of VR, our backpack produceda more realistic jump sensation than the existing approach of onlyvisually modifying the avatar’s jump.

4

IMPLEMENTATION

JumpMod is implemented as a self-contained haptic backpack (i.e.,
works on battery and is wireless)—to accelerate researchers interested in reproducing this device, all designs and code will beavailable and open-source1 .
For sensing jumps, our device couples with existing tracking systems. In all our examples we used HTC VIVE’s positional tracking
for its accuracy and low latency; however, other tracking methods are possible. For instance, in earlier phases of our research,
we explored using a 6-DOF inertial measuring unit (IMU) in the
backpack—we found that the drift of the IMU was problematic whencoupled with our simple jump detection algorithm and would not

suffice for applications requiring high-accuracy in jumping detection (>90%), yet might suffice for applications that do not require
this high-bar of accuracy. Future researchers building on our work
might indeed explore an IMU-only detection but will need to departfrom our simple detection approach towards a more sophisticatedalgorithm that handles IMU drift.

4.1

HardwareOur device’s hardware can be broken down into four main categories: (1) structure, (2) battery as moving weight and source ofpower, (3) drive system, and (4) control.
Structure. The structure of the device is built around three
2020 aluminum extruded profiles, shaped to form the letter “I” asdepicted in Figure 5 (a). We made this structure into a backpack
and improved its ergonomics by adding padded straps (Meister
Backpack Straps) on the four corners as well as hard-foam padded
supports along the aluminum profiles to spread the contact surface
evenly on the user’s back.
Battery as moving weight and as power. Typical weight displacement devices opt for weights made from dense materials (e.g.,lead), which allows the weight to be as small as possible, yet heavy.
However, these devices still need to attach a power source—thelarger the weight the heavier batteries become. While this is not an
issue for grounded devices, this is critical for comfort in wearables.
Instead, in JumpMod, our battery has a double functionality: as
weight and as a power source. To create a battery that satisfied
both requirements perfectly, i.e., one that weighed approximately∼2kg and provided fast current discharge for our motor’s operation, we created a custom battery by manually packing cylindrical
Li-ion cells. Our resulting custom 24V 7Ah battery is made from42 cylindrical Li-ion cells (Anhui Ruikema Li-ion ICR18650-20P5C,
3.7V, and 2000mAh) in a 6S7P configuration, weighing 2kg. It is
enclosed in a box we engineered from five 2mm waterjet-cut aluminum plates and a 3 mm acrylic plate, connected via 3D printed
PLA corner brackets.
Battery life. We measured an average power consumption of∼3.8A in a 707ms motor run. With a 7Ah battery capacity, our
1 http://lab.plopes.org/#JumpMod

CHI ’23, April 23–28, 2023, Hamburg, Germany

device provides about two hours of non-stop operational time—thisis without ever stopping the motor (i.e., “constantly jumping”). Ina more applicable scenario, our backpack would last, for instance,∼12 hours, in a high-paced experience where participants would
jump every 5s.
Drive system. JumpMod is actuated by a brushless DC motor(Flipsky Outrunner 6374, 140kv, 980g, and 3.5kW max power output)
controlled by an electronic speed controller (Flipsky FSESC V4.12
50A). This motor was chosen for its torque output (max 8Nm) ina small and lightweight form factor (980g). Our motor drives theweight using a timing belt pulley (6mm steel core reinforced 2GT)
along a linear rail guide (MGN12H 400mm). Since this weight is
moved relatively fast (707ms to actuate the weight from end-to-end),
the steel core belt is essential to maintain the tension as well as avoid
degradation over time (especially compared to the more commonly
found nylon core belts). A custom 3D-printed PLA tensioner resides
at the other end of the belt allowing us to ensure high tension to
maximize the power transfer from the motor to moving the weight
while avoiding slipping.
Encoder. Our haptic effects are based on the inertia generated
by moving the weight and not by any vibrations generated by theweight hitting the ends of the rail. As such, to move and stop theweight accurately, we implemented an encoder that determines thebelt’s movement. We employ a rotary magnetic encoder (Pololu
Magnetic Encoder Pair Kit for Micro Metal Gearmotors) attached
to an idle pulley, which keeps track of the position of the weight.
Controlling the weight’s position. This encoder allows closing the loop and controlling the weight position precisely by ensuring the weight reaches target positions (rather than just “actuate
for 100ms”). To control this accurately, we implemented a PID controller that maximizes the speed and prevents parasitic movements(overshoots or oscillations).
Microcontroller. The logic in our device is implemented by a
Seeeduino XIAO nRF52840 BLE Sense. This was chosen because of
its embedded sensors and, more importantly, its built-in Bluetooth.
The microcontroller is powered by the electronic speed controller’s
5V line and communicates to it via UART, using the VESC library[55]. While we used external trackers from a VR system (HTC VIVE)
in our studies for more accurate jump detections, users have thepossibility to use its embedded IMU for applications where external
trackers are not possible at the cost of lowered detection accuracy.
Safety. Finally, two limit switches are located at the ends ofthe rail. If these are triggered, the microcontroller stops the motor.
Moreover, since microcontrollers can also be affected by faults,
we added metallic physical stoppers at the ends of the rail, which
ensure that even if the limit switches did not trigger, the weightstays inside the rail.

4.2

Jumping detection algorithm

We detect when users jump using a simple algorithm, based on
absolute head position via an external tracking system. In our experiments, we utilized the HTC VIVE Lighthouse V2 tracking system
and its headset; yet, as mentioned, others expanding on JumpMod
can explore different external tracking systems or even its built-in
IMU for jump detection.

Romain Nith et al.

Since our tracking is based on HTC VIVE, we implemented it
in Unity3D and it communicates wirelessly to our backpack via
Bluetooth (communication latency 68.5ms, see Technical Evaluation
for details). At the start of tracking, our jump detection algorithm
records the user’s height as a reference. In runtime, whenever theuser’s position is below their height by 7.5cm and there is no jumpin progress, the algorithm determines that the user started crouching in preparation for the jump and their position is currently
decreasing. Now, our algorithm is ready to track four jump phases:
launch, ascent, descent, and landing. First, our algorithm triggers
the launch phase when the user’s head position starts increasing
again (extending after crouching). Second, our algorithm triggers
the ascent phase when the user’s height surpasses their reference
height (their feet start to lift from the floor). Third, our algorithm
triggers the descent phase when the user’s height stops increasing
and starts decreasing (they pass the apex of the jump). Finally, ouralgorithm triggers the landing phase when the user’s height crosses
their reference height (the user’s feet are about to touch the floor).
Our algorithm is able to differentiate a real jump (user’s feet left
the ground) from a fake jump (user intended to jump but decided
to abort keeping their feet on the ground) with 94% accuracy (seeUser Study#1 for details).

5

TECHNICAL EVALUATIONS: DEVICE AND
ACTUATION CHARACTERIZATIONS

We conducted two technical characterizations: (1) device characterization (i.e., end-to-end latency, noise, belt slip, and weight), and(2) actuation characterization (i.e., load weight consideration,
kinematics, force, and backlash).

5.1

Device characterization

End-to-end latency. We measured an end-to-end latency of
105.2ms using a 240fps camera. This latency can be further broken
into: (1) 20.0ms for our detection algorithm, (2) 68.5ms of Bluetooth communication from an application to our backpack, and,
finally, (3) 16.7ms for the microcontroller to start moving the weight.
We minimized our Bluetooth latency by preemptively queuing thenext effect and triggering it when needed with a single-byte BLE
characteristic.
Operating noise. We measured the sound generated by our
backpack as it moves the weight along the rail using a decibel meter (Smart Sensor AS824) placed at the height of a user’s ear (quiet
environment, three repetitions per driving direction). We measured
an average peak intensity of 78.1dB. In comparison, propeller-based
vertical force feedback devices tend to be louder. For instance, Virtual Super-Leaping [47] produces 95.3dB at full power. Moreover,
it is worth noting that propeller-based systems actuate for several
seconds (3-10s or more), while our system drives the motor fromend-to-end in ∼707ms. Not only is our device quieter, but it also
produces shorter sounds. One option to reduce the sound of ourdevice even further is to enclose it in a box. Note that this is only
possible in our device, but not in propeller-based devices (need to
be exposed to the air around the user, to push against the air). By
enclosing our device in a generic plastic storage bin (82.5 x 45 x
16.5cm), we measured an average peak sound intensity of 70.3dB

JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

CHI ’23, April 23–28, 2023, Hamburg, Germany—an 8.8dB reduction from a simple plastic storage, we are confident this can be further reduced with a well-engineered acoustic
enclosure.
Force of the belt before slippage (at stall). We found a maximum output force of 45N before our belt system started slipping
during stall. This was measured by pulling the motor against a solidnylon string connected to a load cell (DYMH-103 10kg amplified
by an HX711). This setup measured the maximum force before oneof the components failed. In our case, the maximum was reachedwhen the belt started slipping on the motor shaft. During the measurements, the backpack was laid flat so that no gravitational force
was acting on the moving weight. Finally, this depicts an upper
limit as it was measured horizontally and with the motor outputting
instant torque, i.e., when held upright against gravity, the belt islikely to slip if the stall force exceeds 35N.
Device weight. The total weight of the device is 5.4kg (measured
with a Camry Digital Scale, 50g resolution), including its 2kg load,
which also doubled as our battery to minimize the total weightof the device (see details in Load consideration). For comparison,
Augmented jump weights a total of 60kg, including 24kg of batteries[49]. Virtual Super-Leaping [47] weighs 1.4kg including the batteries
and actuator; while this device is lighter than ours, it requires theuser to hold onto it with their hands, likely to cause muscle fatigue
faster, compared to JumpMod which is worn on the back.

5.2

Actuation characterization

Load considerations. We considered two key factors when choosing the weight of our load: (1) force created by the load as it
accelerates—this creates more momentum (i.e., desired haptic sensations); and (2) backlash-on-landing created by the user’s impact
on the ground as they land, even when the device is not being
actuated—this force is then transferred to the weight and displacesit, creating an unwanted recoil-like haptic sensation with vibrations
and noise. Our device mitigates this by using the motor’s brakemechanism when the weight is not being actuated on the rail—thishelps minimize slipping on impact. The challenge is that these two
factors are at odds with each other—we strived to maximize force(implies using a heavier weight) but, simultaneously, strived to minimize backlash-on-landing (implies using a lighter weight). Figure 6
depicts averages over three repeated measurements: (a) backlashon-landing—measured as the weight’s slip distance relative to thetotal rail length (in %), after a jump while the motor was set to brakemode; and (b) force—measured in Newtons (via F=m·a), using theacceleration data from a 6-DOF IMU (MPU6050) attached directly
to the moving weight as the backpack was affixed to a stand (e.g.,
no jumping and gravity force not subtracted from readings). Bothmeasurements were taken across three different weights: 1kg, 2kg,
and 3kg—weights larger than 4kg are not possible since, as previously mentioned, the belt will slip if the motor is stalled around 35Nof force (or ∼3.5kg weight). We found, as depicted in Figure 6 (a,b), that the 2kg weight struck a better balance between peak force(56.9N) with only 2% of backlash-on-landing, compared to a ∼3x
increase in backlash-on-landing with the 3kg weight. Finally, since
our battery doubled as our weight, our final load was constructedby incrementally adding 3.7V LiPo cells until they reached a total
weight of 2kg.

Figure 6: (a, b) Comparison of trade-offs between different
loads (1kg, 2kg, and 3kg) with regards to (a) backlash-onimpact and (b) peak force. (c) Kinematics graph for our device
moving its 2kg weight from bottom to top, with and without
PID.
Kinematics. Now, having chosen the 2kg weight, Figure 6 (c)depicts the kinematics of its acceleration from bottom to top, withand without our PID controller—measured at peaks of 28.4ms-2 and25.3ms-2 , respectively. Thus, we found a relatively similar peakacceleration across conditions, yet, as we will analyze later, thesudden deceleration is substantially larger without PID. For eachcondition, we measured the kinematics using the same procedure as
in our aforementioned force measurements in Load considerations
with three repetitions per condition (e.g., device fixed to a standand gravity force not subtracted from readings). From this peakacceleration with PID, we determined a 56.9N force generated by
our weight (via F=m·a). This force was perceived by participants
even in the extreme conditions of our Study#2 (i.e., no visuals, nosounds, and spoofed motor action).
Minimizing backlash-on-actuation. When our device actuates, it rapidly accelerates its weight. This driving mechanism left

CHI ’23, April 23–28, 2023, Hamburg, Germany

Romain Nith et al.

alone would result in the weight hitting the ends of the rail andgenerating backlash-on-actuation—an unwanted haptic effect, including an additional force in the opposite direction, noise, andvibrations. To minimize this, our device employs a PID controller.
As depicted in Figure 6 (c), we found that the addition of our PID
controller significantly reduces the backlash-on-actuation by ∼4x:with the PID we found an average peak deceleration of 1.6ms-2 ;conversely, without PID, we found an average peak deceleration of-21.9ms-2 .
Limitations. The characterization presented in this section was
conducted using our hardware. As such, we urge the readers to not
generalize these findings beyond our specific implementation, butinstead, to use these as a reference to compare future variations(e.g., with different motors, rail systems, and so forth).

Tasks. We asked participants to perform two tasks (each 20
jumps): (1) jump-as-you-like-when-you-like and (2) jumpas-fast-as-possible-but-only-on-green—this latter task was designed to see if participants crouching but then deciding not to jump
could trigger false positives. In this task (inspired by the standard“go/no-go” psychophysics design [15]) participants were presented
with a VR traffic light (red, yellow, green). First, the yellow lightwould show up, indicating “get ready and crouch”. After a random
1-2s wait, participants monitored the next light and jumped as fast
as they could in response to seeing it. They were instructed withthe following: if the next light is green, jump; if it is red, rise to
stand as fast as possible but without jumping. The “go” vs. “no-go”
order was randomized (10 of each).

6

Jump detection. For a total of 232 jumps (discarding jumps withcorrupted data from the tracking system, e.g., 4-meter-high jumps,
incomplete jump data points, etc.), in which participants jumped
as they saw fit, we found that our detector was able to recognize
100% of these, including all four phases for each.
Go-no-go detection. For a total of 228 jumps (discarding erroneous data from the tracking system, e.g., 4-meter-high jumps,
incomplete jump data points, etc.), we found 100% detection on
the “go” jumps (all four phases) and 94.0% detection on the “no-go”
jumps (100% launch, 86.2% ascent, 91.4% descent, and 98.3% landing).
Study limitations. We acknowledge that our high recognition
rate is likely due to the straightforward nature of jump detection
using an absolute tracking system—i.e., jumps obey the laws ofphysics, they all require a crouching phase, and they all requireparticipants to push upwards and to overcome their original heightsoon after they leave the ground. Thus, it is straightforward to use
this physics-knowledge and the participant’s height prior to detecting jumps very accurately. Finally, while we asked participantsin the first task to jump-as-they-like, we acknowledge other users(e.g., different body abilities) can exhibit a jump style not detectablewith this approach.

STUDY OVERVIEW

We conducted three studies to characterize and understand theperceived effects of our backpack on participants.
In our first study, we characterized our jump detection accuracy with participants that were not instructed to jump in any
controlled manner (they jumped as they saw fit). We found oursystem detected all four phases of participants’ jumps with 94%
accuracy, including jumps designed to induce false positives (quick
crouches, no jump).
In our second study, we studied what types of sensations
JumpMod evoked. We were especially interested in the sensations created by our device alone, in the absence of any visual orsound stimuli. As such, we actuated our device in all possible configurations (up or down, for all four jump phases, which we callan actuation mode) and measured participants’ feedback as theydescribed their jump sensations. We found that our device, even in
the absence of any visual/audio effects, created five unique jumping
sensations with strong consensus among participants: jump higher,
land harder, land softer, pulled higher, and pulled lower.
Finally, in our third study, we evaluated whether JumpMod
added realism to jump sensations, even compared to existing
VR techniques (e.g., visually exaggerating the avatar’s height). We
found that participants rated the experience and the jumping realism higher while wearing our device, compared to without (baseline).

7.2

8Results

USER STUDY#2: MEASURING THE
PERCEIVED VERTICAL EFFECTS

We conducted a technical evaluation with 12 participants to assess
the accuracy of our simple jump detection algorithm (based on
tracking head position). This study was approved by our ethics
committee (IRB22-0064).

The objective of our second study was two-fold: (1) validate that
it is the movement of the weight that modify a participant’s
jump experience (i.e., does the effect originate from movementof the weight or parasitical weak vibrations or placebo effects?);
and, (2) measure the perceived quality of the induced haptic
effects (i.e., does driving the weight up/down at different phases ofa jump modify the jumping experience?). This study was approvedby our ethics committee (IRB22-0064).

7.1

8.1

7

USER STUDY#1: JUMP DETECTION
ACCURACY

Study design

Apparatus. Participants wore an HMD to track their head position(HTC VIVE Pro Eye and Base Station 2.0).
Participants. We recruited 12 participants (five identified as
female and seven as male; average age=23.8 years old; SD=3.8).
Furthermore, participants’ body mass index (BMI) averaged 22.15(SD=4.11). All participants were healthy with no motor impairment.
Participants were compensated with 10 USD for their time.

Study procedure.

Study design. To best validate that it was the driving of the weightthat modified the user’s perception of their jump, we utilized a standard “oddball” study design [14]. In our oddball study, participants
jumped in a series of four jumps. However, at random, in only oneout of these jumps (at random) our device’s weight was actuatedsignificantly (the oddball). In all the other (non-oddball) jumps, we
spoofed the activity of our device to the best of our capabilities

JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

so that participants could not rely on any other physical cues. We
added vibrations, sounds, and even weak motor movements to create a placebo condition—all of this makes it very hard to identify
which jump was the oddball, and certainly very hard to identify
what was happening in the device. As a result, this study design controlled all the key variables (e.g., extra physical cues like vibration),
except for the oddball jump in which our weight is actuated.
Placebo trials. We spoofed the activity of our device, except
its inertia, using five techniques: (1) Sound spoofing: participants
wore earplugs and noise-canceling headphones that also emitted
white noise. (2) Vibration spoofing: we attached a 600W subwoofer (Kuerl 10 Inch 600W Sub-Woofer) that played sinewaves
at 30Hz, 280Hz, and 980Hz (the main frequencies that our motor
vibrates at, obtained by analyzing the vibrations of our motor using
both a Cold Gold Audio piezo microphone and an MPU6050 accelerometer); as such, in all jumps, participants feel large vibrations
from the subwoofer that masked the (weaker) vibrations from ourmotor. (3) Constant weight/pressure: in all trials, participants
wore the backpack, and our jump sequence was short enough sothat its weight/pressure would be equalized fairly. (4) No distinct
visual feedback: participants faced a tall white wall for the wholestudy, which did not provide any distinct visual features to track
the height of their jumps. (5) Motor always moved: in every jump,
we always actuated the motor at 20% of its power (weak, acted
as placebo) and, only at the oddball trials run at full power—this
20% actuation, which ran in the same direction and for the same
duration as the oddball, created additional vibrations and sounds
that further improved our previous spoofing methods. Putting these
five methods together provided a robust study design, in which
participants could not rely on any additional cues and were left
only with the effect of the large acceleration of the weight acrosstheir back.
Confirming the spoofing. To gather confidence on our spoofing method, we conducted a pilot test with four pilot participants (average age of 27.5 years old, SD=5.92; average BMI of 22.3;
SD=1.60), which were not recruited for the actual study. In thesepilots, we confirmed this method for spoofing was robust as pilot
participants could not accurately identify if the motor was moving(i.e., they guessed at around 50% accuracy, i.e., chance level).
Apparatus. We utilized our backpack (shown in Implementation)
but with a medium-density fiberboard (MDF) backplate rather thanour hollow frame—this helped secure the subwoofer for adequate
spoofing.
Participants. We recruited eight participants (three identified
as female and five as male; average age was 23.5 years old; SD=2.1).
Participants’ body mass index (BMI) averaged at 22.68 (SD=2.49). All
participants were healthy with no motor impairments. Participants
were compensated with 10 USD for their time.

8.2

Actuation modes

We actuated our haptic device in eight actuation modes: two movement directions (up or down the user’s back) × four jump phases(launch, ascent, descent, landing). During the study, the presentation
order of the modes was randomized.

CHI ’23, April 23–28, 2023, Hamburg, Germany

Table 1: Accuracy detection results per actuation mode.
AccuracyMoving weight up

Moving weight
down

LaunchAscentDescent
Landing

88%
75%
81%
79%

91%
66%
94%
86%

8.3

Trial design & metrics

Warmup. Prior to the start of the trials, we gave participants achance to wear our backpack and jump with it until they felt comfortable; this further allowed participants to get accustomed to any
variation in their own jumps.
Trial. A trial was composed of a sequence of four jumps, with
one being an oddball, chosen at random. Per actuation mode, participants performed four trials—a total of 128 jumps per participant(2 directions × 4 phases × 4 jumps).
Metrics. After a single trial (four consecutive jumps with oneof these being an oddball), the participants were asked to verballyanswer two questions: (1) identify the oddball (i.e., which jump felt
distinct from all others) and (2) explain the quality of that jump
using any descriptors associated to jump sensations (i.e., “jumped
higher,” “jumped lower,” “landed harder,” “landed softer,” “longer
airtime,” “shorter airtime,” “pulled higher,” and “pulled lower”). Participants were asked to select which descriptor(s) best represented
the sensation they felt in this trial. If participants selected morethan one descriptor (up to four), we asked them to rank these in descending order of importance. Additionally, participants could also
describe their perceived jump using their own descriptors. Finally,for data analysis, we discarded the descriptors of any incorrectly
identified oddball jumps.

8.4

Results

Feeling the oddball jump. Table 1 depicts our findings regarding
the participants’ accuracy in perceiving the oddballs correctly. We
found the following average detection rates across all participants:
descent-down (driving the weight upwards at the apex of theirjump, 94% accuracy), launch-down (driving the weight downwards
in sync with participants’ jump, 91% accuracy), launch-up (driving
the weight upwards in sync with participants’ jump, 88% accuracy), descent-down (driving the weight upwards as participants
touched the ground, 86% accuracy), descent-up (driving the weight
downwards at the apex of their jump, 81%), landing-down (driving
the weight downwards as participants touched the ground, 79%),
ascent-up (driving the weight upwards as participants’ feet liftedfrom the ground, 75% accuracy), and ascent-down (66% driving theweight downwards as participants’ feet lifted from the ground, 75%
accuracy). Finally, we observed an overall average accuracy score of
81% (SD=8.6%). Our results suggest that in many of these actuation
modes participants perceived jump modulations from our backpack,even in the extreme spoofing conditions we designed for thisstudy—this also suggests that in these conditions the modified
jumping sensation was most likely caused by moving theweight along the participants’ backs.

CHI ’23, April 23–28, 2023, Hamburg, Germany

Romain Nith et al.

Figure 7: Top-three dominant sensations reported for all eight actuation modes (2 weight up/down movements x 4 jump phases).
Quality of perceived sensation. We now present the individual
descriptors to analyze which actuation mode provided uniform
agreement across participants—this is our main finding since theoddballs were purposefully made hard to recognize via our spoofing.
In Figure 7, for the sake of visual clarity, we depict the top-threedominant sensations per actuation mode (unless there were ties,
which in case all ties are shown). Furthermore, we annotated cases
where there was no consensus across participants (i.e., dominantsensations were tied) or weak consensus (i.e., dominant sensations
were close to a tie).
We detailed our results per actuation mode (weight moving
up/down and in the different jump phases):
Launch-up. We found that the two most agreed descriptors
were pulled higher, followed by jumped higher.
Ascent-up. We found that there was no consensus between the
top descriptors, with participants choosing mostly, with an equal
number of votes, between pulled lower, landed softer, and longer
airtime.
Descent-up. We found that there was a weak consensus between
the top descriptors, with participants choosing, with a fairly equal
number of votes, between pulled lower, pulled higher, longer airtime,and shorter airtime. This revealed that this actuation mode shows acontradiction between perceived longer airtime and shorter airtime.
Landing-up. We found that the most agreed descriptor was
landed softer, followed by a weak consensus with pulled lower andpulled higher.
Launch-down. We found that the two most agreed descriptors
were pulled higher, followed by jumped higher.
Ascent-up. We found that there was a weak consensus between
the top descriptors, with participants choosing mostly, with anequal number of votes, between jumped higher, landed harder, andpulled higher.
Descent-down. We found that most agreed descriptors were
pulled higher, followed (not closely) by landed harder.
Landing-down. We found that the two most agreed descriptors
were: pulled lower, followed by landed harder.

8.5

Discussion

Confirming our findings with psychophysics. We observed,
that, even in the extreme spoofing conditions we designed for thisstudy, participants were able to perceive the force added by ourdevice as it accelerated its weight. This is in line with prior workin psychophysics that suggests that participants can notice a 7%
increase (Weber’s fraction) in force when interacting with a background weight of 5.4kg [13]—this is the total weight of our device.
During operation, our device produces a peak force of 56.9N (seeTechnical Evaluation). This force is ∼15x larger than Weber’s fraction for a perceivable force, according to the aforementioned 7%
threshold by [13], and thus noticeable.
Summarizing the results. Figure 8 summarizes the five most
voted jump effects with their actuation mode.

9

CONSOLIDATING STUDY#2 RESULTS INTO
DESIGN RECOMMENDATIONS FOR JUMPEFFECTS

Prospective designers may have different goals when applying our
findings. As such, a prospective designer can traverse the resulting
jump effects, which we depicted in Figure 8, differently, dependingon their design goals.
1. Maximizing unique effects. A prospective designer may beinterested in exploring our device by maximizing the range of jumpeffects it can induce, allowing for more interactive possibilities. By
traversing this table with the goal of maximizing effects, one picks
an actuation mode for each effect to yield the most unique effects—
i.e., not picking the actuation mode that most strongly created eachjump effect but an actuation mode that allows one to maximize thenumber of effects. This yields five possible effects as shown in bluein Figure 9, namely: (1) a sense of jumping higher, (2) a sense ofbeing pulled higher or (3) lower, (4) a sense of landing harder, or(5) softer.

JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

CHI ’23, April 23–28, 2023, Hamburg, Germany

Figure 8: Summary of the five most voted jump effects from Study#2’s findings, with their respective actuation modes.

Figure 9: Recommendation for maximizing unique jump effects, yields up to five sensations.

Figure 10: Recommendation for optimizing strongest jump effects, yields up to four sensations.
2. Optimizing for strongest effects. A prospective designer
might also be interested in optimizing for different goals, e.g., optimizing for the strongest effects rather than for the number ofeffects. In this case, they can traverse the table considering only theactuation mode that most strongly created this jump effect. This
yields four possible effects as shown in blue in Figure 10, namely:a sense of (1) jumping higher, (2) being pulled higher or (3) lower,and (4) landing softer. This type of design is useful for applications
in which the designer wants to elicit only a strong subset of experiences in an environment that might find participants distracted or
overwhelmed with additional cues or tasks.
Even more sensations using multimodal combinations.
Again, we emphasize that we focused on the sensations that ourdevice itself can create, even in the absence of additional audiovisualcues. As it is often the case in multimodal interfaces, the additionof visuals (e.g., VR effects that render a sense of movement), or
audio (e.g., sounds that suggest landing harder/softer, etc.) effects
are likely to become even more unique and diverse (i.e., yielding
potentially a larger set of effects). However, we purposefully explored the haptics-only route since it provides a larger application
scope, i.e., these results are applicable to VR as well as to interfaces
where rendering additional modalities is simply not possible, as
we will demonstrate in the example of our two interactive-sports
applications.
Using these recommendations for our VR experience. Finally, we depict the usage of these recommendations by detailing
how we used this table to design the VR jumping experience usedin our third study. We wanted to increase the player’s immersion as
they: (1) jumped over obstacles with a power-up, (2) were slowed
down by strong winds even during their jumps, (3) were slowed

down by landing on mud terrain, and (4) smashed pumpkins tocollect points. Ideally, these effects would, respectively, yield sensations of: (1) a bigger jump than usual due to the power-up, (2) asense of being pushed down by the wind, (3) a feel of a softer returnto the ground due the mud, and (4) a sense of impact to indicate
they successfully crushed a pumpkin. We then traversed the recommendations table to find which actuation modes render thesesensations. This yielded, respectively, the following commands thatVR experience sends to the backpack: (1) launch-up command forjumping higher (boost), (2) land-down command for being pulledlower (wind), (3) land-up command for landing softer (mud), and(4) descent-down command for landing harder (pumpkin).

10

USER STUDY#3: INTERACTIVE USE OFOUR JUMP EFFECTS IN A VR APPLICATIONIn our third and final study, we turned to measuring the impact ofour jump effects in an interactive context. This study was approvedby our ethics committee (IRB22-0064).
Apparatus and VR experience. Participants wore the JumpMod backpack (detailed in Implementation) and an HTC VIVE VR
headset. We immersed participants in a modified version of the VR
jumping experience (shown Walkthrough), that asked participants
to collect cookies (similar to collecting coins in popular games by
walking into it) and smash pumpkins for four minutes (used to makeboth conditions comparable). Participants experienced four jumpeffects, namely: jumping higher (power-up), being pulled lower(wind), landing softer (mud), and landing harder (pumpkin)—thedesign rationale for these effects is described in Using these recommendations for our VR experience; in short, we rendered four effects
encompassing both midair haptic effects (e.g., jumping higher &CHI ’23, April 23–28, 2023, Hamburg, Germany

pulled lower), as well as landing haptic effects (e.g., landing harder& landing softer). For fairness with the baseline condition, any jumpeffect was rendered in all modalities afforded by the VR headset,
i.e., we added jumping sounds as well as a 4x multiplier for theavatar’s jump height when the jump powerup was active. Finally,
to fully test our device in an interactive context we also chose to
test a key limitation, the situation in which the backpack’s weightneeds to be reset prior to the next jump. As such, our modified VR
experience featured an earthquake effect (VR view shaking andsound of an earthquake) accompanied by a back-and-forth rocking
motion of the weight—this was used anytime the VR determinedthat the weight needed to be moved to the opposite endpoint prior
to the next jump (see Walkthrough for the three ways a designer canchoose to obfuscate the preparation of the weight). While we could
have designed the experience for the easiest method (imperceptiblyslow-moving weight) we opted to, instead, explore a faster weightreset that has less impact on the timing of fast-paced experiencessuch as this VR experience.
Participants. We recruited 12 participants (four identified as
female and eight as male; average age was 26.8 years old; SD=7.83).
Participants reported an average body mass index (BMI) of 21.96(SD=3.91). All participants were healthy with no motor impairment.
Participants were compensated with 10 USD for their time.
Conditions. Participants played our VR experience twice,
one for each interface condition: with JumpMod and without(baseline)—condition order counterbalanced across participants. In
both conditions the VR experience was completely identical: allsame visuals, sound effects, and all power-up jumps scaled up by
4x.
Metrics. At the end of each trial, participants rated, using a
7-point Likert scale, their perceived immersion (1=not immersed,
7=fully immersed) and perceived jumping realism (1=not realistic, 7= indistinguishable from reality). Moreover, after experiencing
both conditions, participants were asked to choose their preferredcondition and explain why. Finally, we asked them to optionallyprovide any comments on the experience.

10.1

Results

Perceived immersion & jumping realism. To analyze any potential difference between wearing our device against the baseline, withregards to perceived immersion and jumping realism, we utilized
a two-tail paired t-test (independent Likert data, following a normal distribution, with a small sample size). We found a statisticallysignificant difference (p<0.005) between immersion when comparing JumpMod (M=5.58 and SD=0.67) and the baseline (M=4.25 andSD=0.87) as depicted in Figure 11 (a). Moreover, we found a statistically significant difference (p<0.001) between jumping realism
when comparing JumpMod (M=4.67 and SD=0.98) and the baseline(M=3.42 and SD=0.67).
Preferred interface condition. In addition to jumping realism
and immersion, we measured participants’ preferred interface condition and found an overwhelming majority preferring JumpMod(11 out of 12 participants) as depicted in Figure 11 (b).
Participants’ commentaries. 8 (out of 12) participants statedthat JumpMod added immersion (P1, P2, P3, P4, P7, P8, P10, P12).
For instance, P10 stated, “backpack adds greater significance toRomain Nith et al.

Figure 11: (a) Perceived immersion & jumping realism reported on a Likert scale; (b) Participants’ preferred interface.
the game, makes it more interesting.” P2, who started with JumpMod, compared the two conditions by stating “I missed having the
backpack on the [baseline].” As expected, three participants (P1,
P6, P11) commented that wearing our backpack is more restrictive
than wearing nothing. For instance, P11 stated “[it] affected jumps”
but added, unprompted, that “[I am] willing to lose comfort but
gain more immersion.” Surprisingly, one participant (P3) reported,in the final interview, that they experienced cybersickness in thebaseline condition, stating “feels like [the VR system] is scaling
my jump.” However, P3 reported no cybersickness in the JumpMod
condition—while this could be due to the added feedback, our study
was not designed to test cybersickness and we do not suggest interpreting from a single data point that JumpMod can alleviate that.
Finally, no participant commented negatively on the reset of theweight—they were not aware that the earthquake effect was used
to reset the weight when needed. In fact, we only observed positive
comments about this effect (P3, P7, P12), in line with comments forall other in-game experiences, such as “[it] feels really real when
the ground starts cracking!” (P3).
Study interpretation & limitations. Our results suggest that
JumpMod enhanced the immersion of a VR experience that makes
use of jumping input, as well as improved the realism of the key interactions in this experience, i.e., jumping. Moreover, we found that,
overwhelmingly, participants preferred our device, even if they still
felt its added weight, was a limitation. We emphasize that our study
is not without limitations and warrants against generalizations
beyond this. First, no singular experience can capture the richnessof all potential interactive experiences that could use jumping as a
key interactive mechanism. Secondly, while our study population
exhibits some variance in participants’ BMI, it was not exhaustive,
and participants with different body abilities were not recruited.
Finally, we only measured the immersion and realism reported byparticipants. We acknowledge that other haptic dimensions were
not measured, such as Harmony, Expressivity, or Autotelics (from[26]).

11

DEMONSTRATING JUMPMOD IN
FREE-WALKING VR AND
INTERACTIVE-SPORTS

We implemented four applications to demonstrate JumpMod: (1)an in-place VR farm jumper (presented in the Walkthrough), (2) aJumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

free-walking VR escape-room, (3) a jump-rope trainer, and (4) aninteractive-basketball game.

11.1

Free-walking & controller-based VR
escape-room

Our first application demonstrates a VR escape room, in whichusers solve puzzles by walking and jumping. Figure 12 depicts
the interaction in the first of four rooms we designed: (a) the key
to solving the puzzle is out of reach even if the user jumps; (b)they find a trampoline, which they grab using the VR handheld
controllers; (c) by jumping off the trampoline, (d) they jump higher
and grab the key—not only they see a higher jump, but our device
creates the feeling of jumping higher. Note that our device is able
to render these vertical sensations without encumbering the user’s
walking (e.g., motion platforms [3], pulleys [27]) or their hands(e.g., handheld propellers [47]).

CHI ’23, April 23–28, 2023, Hamburg, Germany

JumpMod as shown in Figure 13 (b), the user feels the rope’s position
rendered via JumpMod (as the rope moves up, the weight moves
up, after the rope reaches its apex, the weight, conversely, moves
down). This haptic assistance is possible because (unlike propellerbased effects) JumpMod can actuate in two directions (up & down).
To enable this experience, we also engineered the rope tracker,
shown in Figure 13 (d), which is modeled after existing devices[53]. It detects the revolution of the rope using a magnetic encoder
attached to the rope’s free-spinning bearing and communicates via
Bluetooth.

Figure 13: (a) A user attempts to jump rope but messes up thetiming and gets tangled. (b) JumpMod helps the user by (c)moving the weight in sync with the rope’s position. (d) Our
device enables smart jump-ropes to also provide users with
output.

11.3

Figure 12: (a) The key is out of reach even if the user jumps
to their fullest. (b) They find and move a trampoline, so that(c) by jumping on it (d), they can jump higher and grab the
key—they see themselves jumping higher and also feel it with
our device.

11.2

Jump-rope trainer

In this application, we demonstrate JumpMod as the key component
in the user’s interactive experience, without additional UIs. Figure
13 (a) depicts a user, practicing with a smart jump rope trainerthat tracks the rope’s position. The user attempts to jump rope but
messes up the timing and gets tangled with the rope. By wearing

Interactive basketball gameIn our final application, we explored our device in a sports experience where users move around. In this example, two users areplaying an interactive basketball game in which they take turns
wearing our device and using it as the interactive component of
their experience. This experience is made possible by a simple smartphone application we implemented that can send jump commands
to our device (implemented using Android SDK [2] and Nordic BLE
framework [41]).
Figure 14 depicts the interaction: (a) the opponent, not wearing
our backpack, tries to block the player from shooting the ball; (b)seeing they missed the opportunity to block, they tap on a smartphone in their armband, to send the player a debuff (a defensive
measure to affect the other player), which causes our backpack
to render the sensation of pulled lower—negatively affecting theirjump when shooting for the hoop; (c) later, the opponent misses
another block opportunity, but this time they have exhausted their
allowed number of debuffs, so they send a wildcard (a riskier move,

CHI ’23, April 23–28, 2023, Hamburg, Germany

since it could either be a debuff or a power-up)—unfortunately, thisbackfires and favors the player wearing JumpMod by renderinga sense of jumping higher—positively affecting their jump whenshooting for the hoop.

Romain Nith et al.

pulled higher. We demonstrated these effects in four applications,
including virtual reality and interactive sports.
Finally, as future work, we believe the haptic mechanism behind JumpMod can be extended to create further sensations and be
combined with different types of actuators or onboard sensing systems. To accelerate these explorations, we will provide the complete
source code and hardware designs as open source1 .

REFERENCES

Figure 14: (a) Interactive basketball with two players. Players
take turns wearing the backpack or the smartphone on theirarm to send “attacks” to each other, affecting the way theiropponent jumps as they shoot the ball to the hoop to scorepoints such as (b) pulled lower as a debuff or (c) jump higher
as a power-up.

12

CONCLUSIONS AND FUTURE WORK

We proposed, implemented, and validated a wearable device, which
we call JumpMod, that modulates the user’s perceived jump. JumpMod achieves this by moving a weight along the user’s back, whichcauses an inertial force that modulates the user’s perception of theirown vertical momentum. Unlike existing approaches (e.g., wearingor holding large propellers to gain vertical momentum, or sittingor standing on grounded motion platforms), JumpMod modifies
the user’s perceived jump using its small form factor, small enoughthat it is worn as a backpack—it is completely untethered, users
can walk around freely and do not need to hold any handles toexperience its haptic effects.
Throughout three user studies and a technical evaluation, wecharacterized JumpMod. Most importantly, we found, in our seconduser study, that JumpMod creates five distinct sensations: (1) asense of jumping higher, (2) sense of being pulled lower, (3) a senseof landing harder, (4) a sense of landing softer, and (5) a sense of[1] [1]Altimira, D., Mueller, F. “Floyd,” Clarke, J., Lee, G., Billinghurst, M. and Bartneck, C. 2016. Digitally Augmenting Sports: An Opportunity for Exploring andUnderstanding Novel Balancing Techniques. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, May 2016),
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