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

Table 4: Recognition accuracy of both segmentation methods using different classification algorithms. Figure 8: Performance comparison of each activity using: (**a**) Recall; (**b**) Precision; (**c**) F-measure. in all performance measures used in this work. This enhancement reflects the effectiveness and applicability of the method on continuous data collected from a single accelerometer. The algorithm enabled the accurate classification of similar activities, such as abduction and flexion. In contrast, when using sliding window segmentation, the algorithm frequently confused these activities and experienced difficulties in recognizing them. This demonstrates that the impact of the correct segmentation of raw data is not only on performance but also on model robustness. Table 4 shows that the new segmentation method achieved a recognition rate of more than 91% using four different ML classifiers, and SVM outperformed the others. This is consistent with expectations because SVM is highly regularized and works effectively with small datasets and few classes. Moreover, the results of this table emphasize the effectiveness of the proposed method, which outperformed the sliding window method across all four classifiers with an average of 5.5%. The results clearly show that wearable sensors are a promising technology for monitoring and performing automated rehabilitation assessments. Despite the performance enhancement obtained using specific sensor types, affordability and usability are also important factors for determining their applicability. The study in [18] used sEMG electrodes to recognize different activities performed by stroke patients. Although the results suggested that sEMG signals provide good accuracy in upper-limb activities, attaching these electrodes is a sensitive process that requires an expert. This type of sensor is impractical for use in certain applications, including monitoring in-home rehabilitation, especially if the set of activities must be repeated daily or multiple times during the day. Contrastingly, the accelerometers used in this research are low-cost and easy-to-use sensors. This work can be considered as a systematic approach to dynamic signal segmentation, which could be applied to other types of physical activity. However, slight modifications should be taken into account when needed. For the segmentation of a wider range of activities, more signal characteristics might be needed. One possible solution is to exploit statistical and time series analysis to detect the signal variation. The new method presented in this paper overcomes the limitation of the sliding window approach through the adaptive segmentation of physical activities. However, we acknowledge that certain limitations are evident in our work. First, only an accelerometer was used for physical activity recognition. Although studies have proven the effectiveness and efficiency of accelerometers, additional types of sensors, such as gyroscopes and magnetometers, may improve recognition performance. Second, this work focused on the segmentation of physical activities applied during the rehabilitation of SCI patients. Further research should be undertaken to study the effect of this method on other physical activities. Third, the data were collected in a controlled environment. Future work might consider collecting data from real scenarios in which participants perform activities at home. Finally, the selection of a threshold value depends on the training data. In future work, the threshold could be chosen with the ability to update periodically according to the incoming signal. In addition to the abovementioned future work, the impact of the method on the rest of the activities will be investigated. In addition, frequency-domain features and additional time-domain features will be identified to facilitate performance enhancement. Finally, the method will be introduced into hospital-based rehabilitation sessions to examine the performance on SCI individuals.