# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
## 4 Proposed Method
### Determining Segment Boundaries
## 8 Conclusions

In physical activity recognition using machine learning algorithms, data segmentation is an essential step that may influence accuracy. Nevertheless, studies mostly adopt the sliding window technique and rely on the window size used in previous works. Although this approach is considered simple, it might be ineffective, especially for activities with different durations. This study proposed a novel segmentation method that can be applied to enhance the recognition of physical activities performed in a rehabilitative context. To adaptively segment the raw data, the algorithm identifies the time boundaries to represent the start- and endpoints of each activity. Peak boundaries and valley boundaries are used depending on the signal characteristics. The proposed algorithm was also verified in this paper. The results, which were generated using data from a single accelerometer located on the wrist, approved the effectiveness and applicability of the method on continuous raw data. Moreover, adopting the proposed method generally improved recognition performance, and the improvement was more substantial for similar activities. Conceptualization, N.A. ; methodology, N.A. ; software, N.A. ; validation, N.A. ; formal analysis, N.A. ; investigation, N.A. ; resources, N.A. ; data curation, N.A. ; writing--original draft preparation, N.A. ; writing--review and editing, N.A. and H.A.-D.; visualization, N.A. ; supervision, H.A.-D.; project administration, H.A.-D. All authors have read and agreed to the published version of the manuscript. The authors would like to thank the Deanship of scientific research in King Saud University for funding and supporting this research through the initiative of the DSR Graduate Students Research Support (GSR). The authors declare no conflict of interest. ## References

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