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Currently, abnormal gait behavior is primarily recognized by pressure and inertial data obtained from wearable sensors. However, the data drift and wearing difficulties for patients have impeded the application of these wearable sensors. Here, we propose a contactless abnormal gait behavior recognition method that captures human pose data using a monocular camera. A lightweight OpenPose (OP) model is generated with Depthwise Separable Convolution to recognize joint points and extract their coordinates during walking in real time. For the walking data errors extracted in the 2D plane, a 3D reconstruction is performed on the walking data, and a total of 11 types of abnormal gait features are extracted by the OP model. Finally, the XGBoost algorithm is used for feature screening. The final experimental results show that the Random Forest (RF) algorithm in combination with 3D features delivers the highest precision (92.13%) for abnormal gait behavior recognition. The proposed scheme overcomes the data drift of inertial sensors and sensor wearing challenges in the elderly while reducing the hardware requirements for model deployment. With excellent real-time and contactless capabilities, the scheme is expected to enjoy a wide range of applications in the field of abnormal gait measurement.<\/jats:p>","DOI":"10.3390\/s22114070","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement"],"prefix":"10.3390","volume":"22","author":[{"given":"Yuliang","family":"Zhao","sequence":"first","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoai","family":"Wang","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Life Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Shan","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianjiang","family":"Li","sequence":"additional","affiliation":[{"name":"Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China"},{"name":"Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"Shijiazhuang School, People Liberation Army Engineering University\u2014Shijiazhuang, Shijiazhuang 050003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109483","DOI":"10.1016\/j.mehy.2019.109483","article-title":"Automated Parkinson\u2019s Disease Recognition Based on Statistical Pooling Method Using Acoustic Features","volume":"135","author":"Yaman","year":"2020","journal-title":"Med. 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