# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 2 Related Work
### Finger-Mounted Systems  
[PERSON] et al. (2011) present a gesture recognition system that uses a smart ring to record data. The ring consists of a 9-axis sensor, a battery and a proximity sensor to sense adjacent fingers. It transmits data via Bluetooth and does not have any mechanism to start or stop the transmission, which indicates that they performed the segmentation manually by hand. The results were evaluated by comparing four different machine learning models (multi-layer perceptron, decision tree, naive Bayes and support vector machine), using cross-validation, whereas the data were collected with 24 participants, each performing 9 different gestures. It is notable, that air-gestures perform better than gestures performed against a fixed surface--this supports the approach of this paper using air-gestures. [PERSON] et al. (2012) propose a rule-based extensible gesture recognition system. This system is based on a smart ring with a 3-axis accelerometer that also contains a vibrator for tactile feedback. Data are transmitted via Bluetooth Low Energy with a sensor sampling rate of 50 Hz. The accelerometer records data within a range of \(-8\) g to \(+8\) g, which should suffice for the gesture catalog they propose. Since all gestures are defined as 2D gestures and the ring coordinate system is always aligned with the gestures, one axis is ignored, resulting in a 2D space. The gesture catalog encompasses eight basic gestures and 12 complex gesturescomposed of a combination of two to three basic gestures. The hamming distance is used as a measure for the similarity between two gestures. In contrast to machine learning-based approaches, the recorded data are only used to test the accuracy by counting the number of correct matches. The basic gesture matching resulted in an accuracy of 98.9% and the complex matching in 97.2%. However, some of the gestures were not detected by the segmentation algorithm, because some participants could not reach the minimum acceleration that is required for the thresholds. The advantage of this approach over machine learning approaches is that it is not necessary to train a model beforehand, which leads to an extensible system. However, it is not robust against different users and has strict constraints on the orientation of the ring and how the gestures have to be performed. The magic ring [13] is a wired sensor that is mounted on the middle segment of the index finger. The mounting location was chosen, because the middle segment is the most flexible and should therefore deliver better results. The ring contains a 3-axis accelerometer which provides values in a range from \(-1.5\) g to \(+1.5\) g. This range is very small compared to most other systems, but the gesture catalog contains only very fine grained gestures. The gesture catalog consists of 12 simple gestures. Six of the gestures are targeted at finger movement while the hand is fixed and four are targeted at hand movements. The remaining two gestures represent bending and unbending movements of the finger. The data were recorded with 20 participants, each repeating every gesture five times, thus resulting in 100 samples per gesture and 1200 samples in total. Since there is no mechanism to activate or deactivate the recording between gestures, the data were segmented by using a fixed window with an empirically estimated size of one second. The results were evaluated by three different machine learning algorithms (C4.5, KNN and NB) using cross-validation. Instead of using all features, different groups of features were evaluated with every model to determine the feature quality. It is interesting that fewer features can produce better results. Therefore, a feature elimination strategy may be required for other systems as well. Relative features could be further improved and their impact on accuracy implies that experimentation may be necessary in that regard. The recognition accuracy could be further improved by using more complex models. [PERSON] et al. [14] propose a system to control robots with gestures.