# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 2 Related Work
### Wrist-Mounted Systems  
[PERSON] et al. [15] propose a gesture recognition system that utilizes a smartwatch. The watch contains a 3-axis accelerometer, which provides values in a range from \(-2\) g to \(+2\) g. This is a relatively small range compared to other systems. The data are sampled with 100 Hz and sent to a tablet for processing via a wireless RF-transmitter. The data were recorded from five people, each performing four different gestures. Each gesture was repeated five times, which results in 25 samples per gesture. This is a relatively small sample size compared to other systems. Nevertheless, the goal is to examine algorithms that require a small training data set. Twenty statistical features are derived from the data and evaluated with a feature weighted Naive Bayes and a Dynamic Time Warping algorithm. The results show that the NB algorithm performs best with 97% accuracy. The DTW performance is very similar with 95%. [PERSON] et al. [9] propose a gesture-controlled user interface for visually impaired people. The system uses a smartwatch to recognize gestures, which instructs a smartphone to carry out various tasks. The watch contains a 3-axis accelerometer with a sampling rate of 10 Hz. This sampling rate originates from API restrictions of the smartwatch and is very low compared to other systems. The data are sent to the smartphone via Bluetooth to recognize the gestures. Eight different gestures were recorded by 15 different users, each repeating every gesture for 15 times, thus resulting in 225 samples per gesture and totaling 1800 samples. The data transmission starts when the user presses the display of the watch. The data are directly used as input for two support vector machine approaches and a dynamic time warping approach. The proposed kernel for the SVM approach is an approximated version of the global alignment kernel and has lower computational cost than the standard version. The optimal parameters for the kernels were estimated with grid search and cross-validation. The results show that the faster kernel approach delivers a slightly lower accuracy than the standard approach. The former results in an average accuracy of 91.08% and the latter in 92.33%. The DTW approach performed worse than both SVM approaches with 54.89%. The low accuracy of the DTW approach suggests that it is not suitable for a gesture recognition task without feature engineering. [PERSON] et al. propose a system, which is able to recognize multi-finger and hand gestures with a wrist-mounted sensor [16]. The sensor contains an accelerometer and gyroscope, which measure hand movements as well as tendon movements to recognize hand and finger gestures, respectively. The data are sampled with 128 Hz and are transmitted to a smartphone using Bluetooth. The gesture catalog consists of 37 gestures, where 13 are finger gestures, 14 are hand gestures and 10 are arm gestures. Only 10 samples were recorded per gesture, totaling in a data set with 370 gestures. During the recording, the finger, hand and arm were fixed to a chair depending on the gesture. Feature extraction is performed for various feature categories, such as motion energy, posture, motion shape and motion variation. The 10 best performing features are selected by determining the information gain. Three different classifiers (naive Bayes, linear regression and decision tree) are evaluated based on the selected features. The algorithms are only tested with a very small sample size of 10 samples per gesture and in a very controlled environment. To test the system's robustness, a much larger sample size, recorded with different users, would be necessary. Serendipity [17] is a system to recognize fine finger gestures with a smartwatch. The smartwatch contains an accelerometer and a gyroscope and is capable of sensing rotation and gravity due to the sensor fusion between these two. The sensors are sampled with 50 Hz and the data are sent via Wi-Fi to a server for processing. Five fine-grained multi-finger gestures were recorded with 10 participants.