In-Home Gait Abnormality Detection Through Footstep-Induced Floor Vibration Sensing and Person-Invariant Contrastive Learning

作     者：Dong, Yiwen Kim, Sung Eun Schadl, Kornel Huang, Peide Ding, Wenhao Rose, Jessica Noh, Hae Young 

作者机构：Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA Lucile Packard Childrens Hosp Mot & Gait Anal Lab Menlo Pk CA 94025 USA Stanford Univ Dept Orthopaed Surg Redwood City CA 94063 USA Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA 

出 版 物：《IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS》 (IEEE J. Biomedical Health Informat.)

年 卷 期：2024年第28卷第12期

页      面：7054-7067页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：U.S. National Science Foundation [NSF-CMMI-2026699] Stanford Blume Fellowship 

主　　题：gait disorders Gait assessment sensors sensors vibrations vibrations vibrations contras- tive learning contras- tive learning contras- tive learning contras- tive learning contras- tive learning sensors vibrations 

摘      要：Detecting gait abnormalities is crucial for assessing fall risks and early identification of neuromusculoskeletal disorders such as Parkinson s and stroke. Traditional assessments in gait clinics are infrequent and pose barriers, particularly for disadvantaged populations. Previous efforts have explored sensor-based approaches for in-home gait assessments, yet they face limitations such as visual obstructions (cameras), limited coverage (pressure mats), and the need for device carrying (wearables and insoles). To overcome these limitations, we introduce an in-home gait abnormality detection system using footstep-induced floor vibrations, enabling low-cost, non-intrusive, device-free gait health monitoring. The main research challenge is the high uncertainty in floor vibrations due to gait variations among people, making it challenging to develop a generalizable model for new patients. To address this, we analyze time-frequency-domain features of floor vibration data during specific gait phases and develop a feature transformation method through contrastive learning to address the between-people gait variation challenge. Our method transforms the features from vibrations to an embedding space where samples from different people stay close to each other (robust to people variation) while normal and abnormal gait samples are far apart (sensitive to gait abnormalities). Then, gait abnormalities are detected by a downstream classifier after feature transformation. We evaluated our approach through a real-world walking experiment with 21 participants and achieved an 85% to 95% mean accuracy in detecting various gait abnormalities. This novel method overcomes prior limitations in in-home gait assessments, offering accessible gait abnormality detection without the need for intrusive devices or labels for new patients.