# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 2 Related Work
### Discussion  
Table 1 shows an overview of the compared systems. Half of the systems use wrist-mounted sensors or smartwatches, while the other half uses finger-mounted sensors or smart rings. Every sensor device includes at least an accelerometer. Half of all systems additionally measure data with a gyroscope. However, the gesture catalog of some systems does not require a gyroscope because none of the gestures contain rotation movements. Nevertheless, some systems could benefit from an additional gyroscope.  
Generally, the accuracy ranges from 82.3% to 98.57% and leads to an average accuracy of about 92%. It is not clear from the comparison whether wrist- or finger-mounted sensors produce better results. Moreover, this cannot be directly compared because different systems use different recognition techniques and gesture catalogs. Furthermore, some systems base the performance of the models on user-independent and some base it on user-dependent experiments and facilitate different feature sets or no features at all. To compare two mounting positions, the experiments need to be identical in every possible way, so that unknown influences do not disturb the results.  
All the findings of this section directly influence the methodology of this paper. In order to compare the mounting points, the experimental setup needs to be identical. Therefore, the data for both locations will be recorded in parallel, using a button-based mechanism to start and stop the gesture. The same gesture set will be used for both devices, containing only hand gestures. In order to examine all aspects of gestures, the catalog will contain gestures that are performed in a 3D space, utilizing all axes of the sensors. Both devices will use 9-axis sensors, containing an accelerometer, gyroscope and magnetometer. The latter will be ignored to prevent interference with other smart devices. To support the design aspects of a real-time system, both devices will transmit the data wirelessly to a smartphone for processing. A sampling rate of 100 Hz will be used, which should be low enough to be transmitted in real-time and high enough to capture most of the movement patterns.  
For the data acquisition, 10 participants will provide 10 repetitions of 12 gestures, which results in 100 samples per gesture for each mounting location and is consistent with the gesture and sample size that is used in the proposed systems. The feature derivation process will produce different features, which will be ranked by importance and the unimportant ones will be removed. Only some models in the proposed systems, will be used for the evaluation, since the models depend on the compatibility with machine learning frameworks.  
Since all systems achieved an average accuracy of 92%, the desired minimum for this paper matches this number. To examine alternative manual segmentation techniques, the whole recognition process is repeated with a different button-based mechanism.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Publication** & **Mounting Location** & **Nr. of Gestures** & **Nr. of Samples** & **Sensors** & **Model** & **Accuracy** \\ \hline [PERSON] et al. [11] & Finger & 9 & 120 & A,G & MLP & 97.80\% \\ [PERSON] et al. [12] & Finger & 8 & 70 & A & SM & 98.90\% \\ [PERSON] et al. [13] & Finger & 12 & 100 & A & DT & 86.90\% \\ [PERSON] et al. [14] & Finger & 5 & 150 & A,G & HMM & 82.30\% \\ [PERSON] et al. [15] & Wrist & 4 & 25 & A & DTW & 95.00\% \\ [PERSON] et al. [9] & Wrist & 8 & 225 & A & SVM & 93.33\% \\ [PERSON] et al. [16] & Wrist & 14 & 10 & A,G & NB & 98.57\% \\ [PERSON] et al. [17] & Wrist & 5 & 800 & A,G & KNN & 87.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the compared systems.