Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer

2017 ◽  
Vol 38 ◽  
pp. 41-59 ◽  
Author(s):  
Mohd Halim Mohd Noor ◽  
Zoran Salcic ◽  
Kevin I-Kai Wang
Keyword(s):  
Physical Activity ◽  
Activity Recognition ◽  
Sliding Window ◽  
Physical Activity Recognition

scholarly journals Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor

2021 ◽  
Vol 11 (6) ◽  
pp. 2633
Author(s):  
Nora Alhammad ◽  
Hmood AlDossari
Keyword(s):  
Physical Activity ◽  
Activity Recognition ◽  
Spinal Cord Injuries ◽  
Window Size ◽  
Sliding Window ◽  
Machine Learning Techniques ◽  
Accelerometer Sensor ◽  
Dynamic Segmentation ◽  
Physical Activity Recognition ◽  
Window Approach

Data segmentation is an essential process in activity recognition when using machine learning techniques. Previous studies on physical activity recognition have mostly relied on the sliding window approach for segmentation. However, choosing a fixed window size for multiple activities with different durations may affect recognition accuracy, especially when the activities belong to the same category (i.e., dynamic or static). This paper presents and verifies a new method for dynamic segmentation of physical activities performed during the rehabilitation of individuals with spinal cord injuries. To adaptively segment the raw data, signal characteristics are analyzed to determine the suitable type of boundaries. Then, the algorithm identifies the time boundaries to represent the start- and endpoints of each activity. To verify the method and build a predictive model, an experiment was conducted in which data were collected using a single wrist-worn accelerometer sensor. The experimental results were compared with the sliding window approach, indicating that the proposed method outperformed the sliding window approach in terms of overall accuracy, which exceeded 5%, as well as model robustness. The results also demonstrated efficient physical activity segmentation using the proposed method, resulting in high classification performance for all activities considered.

scholarly journals Accuracy of physical activity recognition from a wrist-worn sensor

Physiotherapy ◽  
2017 ◽  
Vol 103 ◽  
pp. e47
Author(s):  
K. Cooper ◽  
S. Sani ◽  
L. Corrigan ◽  
H. MacDonald ◽  
C. Prentice ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Recognition ◽  
Physical Activity Recognition
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