Abstract
Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.
Highlights
Osteopenia and sarcopenia can cause various senile disorders and are key factors related to the quality of life in old age [1–3]
The identification results of 21 osteopenia and 21 non-osteopenia subjects showed the highest accuracy in support vector machine (SVM) when 24 spatial–temporal parameters were used as inputs, but showed an accuracy of less than 65%
Using an inertial sensor as an input for deep learning, ResNet showed the highest accuracy among convolutional neural network (CNN), BiLSTM, and ResNet
Summary
Osteopenia and sarcopenia can cause various senile disorders and are key factors related to the quality of life in old age [1–3]. Portable tools and methods that can analyze osteopenia and sarcopenia risks in our daily lives, rather than requiring a specialized hospital setting, can be considered. Osteopenia is a cause of fracture and increases the risk of complications, in addition to pain caused by fractures. Patients with sarcopenia have a slow gait, reduced muscular endurance, face difficulty in daily living, and frequently need help from others. Osteoporosis, falls, and fractures can occur whereas the blood and hormonal buffering action of the muscle are moderated, reducing the basal metabolic rate, making chronic diseases unmanageable, and increasing the likelihood of aggravating diabetes and cardiovascular disease [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
