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Tian Tian,1 Cheng Wang,2– 4 Yuan Xu,5 Yuzhi Bai,1 Jing Wang,1 Zhou Long,2,3 Xiangdong Wang,6,7 Lichun Zhou8 1General Practice Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Institute of Digital Economy Industry, ICT, Hangzhou, People’s Republic of China; 3Luoyang Institute of Information Technology Industries, Luoyang, People’s Republic of China; 4Ningbo Institute of Information Technology Application, CAS, Ningbo, People’s Republic of China; 5Department of Endocrinology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China; 6Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, People’s Republic of China; 7Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, People’s Republic of China; 8Department of Neurosurgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Yuan Xu Email xuyuan3200@sina.comBackground: Previous studies have shown that the gait of patients with type-2 diabetes mellitus is abnormal compared with the healthy group. Currently, a three-dimensional motion analyzer system is commonly used for gait analysis. However, it is challenging to collect data and use in clinical study due to extensive experimental conditions and high price. In this study, we used a wearable gait analysis system (Gaitboter) to investigate the spatial and temporal parameters, and kinematic data of gait in diabetic patients, especially those with peripheral neuropathy. The aim of the study is to evaluate the wearable gait analysis system in diabetic study.Materials and Methods: We conducted a case–control study to analyze the gait of type 2 diabetes mellitus. Gaitboter was used to detect and collect gait data in the ward of Beijing Chao-yang Hospital, Capital Medical University from June 2018 to October 2018. We collected the gait data of participants (N= 146; 73 patients with type 2 diabetes, 16 with peripheral neuropathy and 57 without peripheral neuropathy, and 73 matched controls). The gait data (stance phase, swing phase, double-foot stance phase, single-foot stance phase, walking cadence, stride length, walking speed, off-ground angle, landing angle, maximum swing angle, minimum swing angle, and foot progression angle) in diabetic patients were recorded and compared with controls. SPSS 22.0 statistical software was used to analyzed the gait parameter data.Results: We found that the landing angle and the maximum swing angle of diabetes patients with or without peripheral neuropathy were significantly less than those of the control group (P < 0.05). The walking speed of diabetes patients with peripheral neuropathy is significantly less than those of the control group (P < 0.05).Conclusion: This study confirms that the wearable gait analysis system (Gaitboter) is an ideal system to identify abnormal gait in patients with type 2 diabetes and provides a new device and method for diabetes-related gait research.Keywords: gaitboter, gait analysis, diabetes mellitus, peripheral neuropathy, swing phase |
