# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 5 Concluding Remarks and Future Work  
The proposed SBR algorithm was found to be effective for gesture detection and recognition. In our experiments, smart phones with accelerometers and gyroscopes were employed for the collection of sensory data for training and testing. It could be observed from the experiments that the proposed algorithm attained a high AUROC performance even for small training sets when the ITR ratio values were above 0.2. In addition, the proposed algorithm outperformed exiting object detection algorithms such as CornerNet in terms of AUROC for gesture detection. Furthermore, the proposed algorithm had superior classification accuracy over approaches such as SVM, LSTM, CNN, and Residual PairNet for gesture classification. The proposed algorithm also had a robust average EDA against the selection of thresholds \(T\) in a large range for gesture detection. Even with a large stride size \(\Delta=4\) for the inference, the proposed algorithm was able to achieve an average EDA above 0.8. The algorithm therefore is an effective alternative for sensor-based HMI applications requiring both accurate detection and classification for hand gestures. Figure 10: Examples for the detection and classification of the proposed algorithm. There are three test sequences considered in the example. Each sequence is a mixture of foreground gestures and background samples/gestures. The foreground gestures and background gestures are marked by coloured and dotted blocks, respectively. The PKI or SKI scores resulting in matched detections are marked by dotted lines. Each foreground gesture results in a pair of matched detections for PKI and SKI. Therefore, we are able to detect all the foreground gestures in the examples, (**a**) Example 1, (**b**) Example 2, (**c**) Example 3. An extension of this work is the combination of SBR and VBR for human activity recognition (HAR), where gesture recognition can be considered as a special case. For many applications, it would be beneficial to achieve accurate HAR by exploiting sensors with different modalities, such as inertial or visual ones. While large body movements are differentiated by cameras, small hand actions can be captured by accelerometers. Larger varieties of actions can then be detected and/or classified with the presence of multiple and multimodal sensors. However, actions and/or gestures best-suited to specific applications could still be a challenging issue to be explored in the future. Conceptualization, Y.-L.C. and W.-J.H. ; methodology, Y.-L.C. and T.-M.T. ; software, Y.-L.C., T.-M.T. and P.-S.C.; validation, W.-J.H. and P.-S.C.; resources, W.-J.H. ; writing--original draft preparation, W.-J.H. ; writing--review and editing, W.-J.H. and T.-M.T. ; visualization, Y.-L.C. and P.-S.C.; supervision, W.-J.H. ; project administration, W.-J.H. ; funding acquisition, W.-J.H. All authors have read and agreed to the published version of the manuscript. The original research work presented in this paper was made possible in part by the Ministry of Science and Technology, Taiwan, under grants MOST 110-2622-E-003-003 and MOST 111-2622-E-003-001. Not applicable. The data are contained within the article. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: