MacLean, M.K.; Rehman, R.Z.U.; Kerse, N.; Taylor, L.; Rochester, L.; Del Din, S. Walking bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back. Sensors2023, 23, 8973.
MacLean, M.K.; Rehman, R.Z.U.; Kerse, N.; Taylor, L.; Rochester, L.; Del Din, S. Walking bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back. Sensors 2023, 23, 8973.
MacLean, M.K.; Rehman, R.Z.U.; Kerse, N.; Taylor, L.; Rochester, L.; Del Din, S. Walking bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back. Sensors2023, 23, 8973.
MacLean, M.K.; Rehman, R.Z.U.; Kerse, N.; Taylor, L.; Rochester, L.; Del Din, S. Walking bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back. Sensors 2023, 23, 8973.
Abstract
Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as “walking” or “non-walking”. One algorithm had generic thresholds, while the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82 respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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