ArticleBiosensors2022
Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 4 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review.BMC geriatrics · 2024Pooled it
- Wearable Inertial Sensor-Based Detection of Exercise-Induced Mobility Adaptations in Older Women: A Randomized Controlled Trial.Biosensors · 2026Trial
- Comparing Stability, Gait, and Functional Score after 40-mm Dual-Mobility Hip Arthroplasty to 36-mm Head Hip Arthroplasty in Elderly Hip Fracture Patients.Clinics in orthopedic surgery · 2025Trial
- Altered Neuromuscular Time-Frequency Organization During Sit-to-Walk in Parkinson's Disease: An Explainable Artificial Intelligence Approach Using Surface Electromyography.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence Empowers Physical Therapy for Older Adults: A Perspective.Health science reports · 2026Article
- Article
- Noise-robust markerless video gait anomaly detection via two-stage acquisition and LSTM autoencoders.Scientific reports · 2025Article
- Advances in biomonitoring technologies for women's health.Nature communications · 2025Review
- Classifying simulated gait impairments using privacy-preserving explainable artificial intelligence and mobile phone videos.PLOS digital health · 2025Article
- Interpretable Machine Learning for Osteopenia Detection: A Proof-of-Concept Study Using Bioelectrical Impedance in Perimenopausal Women.Journal of functional morphology and kinesiology · 2025Article
- Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study.Sensors (Basel, Switzerland) · 2025Article
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia.Journal of imaging informatics in medicine · 2025Article
- AN INTELLIGIBLE AI-DRIVEN DECISION SUPPORT SYSTEM FOR POSTSTROKE MOBILITY ASSESSMENT.Journal of rehabilitation medicine. Clinical communications · 2025Article
- Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion.Diagnostics (Basel, Switzerland) · 2024Article
- Wearable Fabric System for Sarcopenia Detection.Biosensors · 2024Article
- Bidirectional mediation of bone mineral density and brain atrophy on their associations with gait variability.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.