Abstract
Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
Highlights
Walking ability is an important consideration during routine therapy treatment and rehabilitation following surgery and is crucial for human mobility, which enables predictions of quality of life, mortality, and morbidity [1,2]
The results showed that a mean (± standard deviation (SD)) amplitude between 51.66 (±7.33) and 80.50 (±0.99) was obtained using the three height-to-width ratio-based body measurements (HW1, HW2, and HW3) calculated from both datasets (Table 2)
The maximum and minimum frequencies obtained for the height-to-width ratio-based body measurements were 8.18 (±0.65) and 2.64 (±0.45), respectively, and those found for the area ratio-based body measurements were 8.10 (±0.65) and 2.47 (±0.58), respectively
Summary
Walking ability is an important consideration during routine therapy treatment and rehabilitation following surgery and is crucial for human mobility, which enables predictions of quality of life, mortality, and morbidity [1,2]. For a long time, walking speed has been used as an independent screening indicator of demographic characteristics (e.g., age and sex), functional activities (e.g., kinematic and kinetic patterns and spatiotemporal parameters), and various physical outcomes (e.g., activity-related fear of falling) in normal controlled individuals (e.g., healthy) and patients (e.g., Parkinson’s disease and osteoarthritis) [6,7,8,9,10]. The functional movement performance of individuals with neuromuscular conditions, such as post-stroke and cerebral palsy, can be assessed based on their walking speed, which might have an impact on gait [9,10]. Walking speed provides a significant contribution to health assessment, including changes in spatiotemporal, kinematic, and kinetic parameters during the gait cycle [13]. The efficient classification of walking speed could play a vital role in the scrutinization of normal and abnormal gait measurements, in gait-based assessments during a rehabilitation process, and might help improve clinical care and our understanding of gait balance
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