Piezoresistive-Based Gait Monitoring Technique for the Recognition of Knee Osteoarthritis Patients

Abstract

Knee osteoarthritis (Knee OA) is a degenerative disease that often perplexes the elderly and the whole society, and its timely recognition receives interest worldwide. However, traditional imaging examinations cannot reflect dynamic function nor implement long-term monitoring. To address this issue, this article suggests a piezoresistive-based gait monitoring method to recognize patients with Knee OA by assessing the plantar pressure signals during the subjects' walking, which is mobile, wearable, low-cost, and convenient. Eighteen subjects diagnosed with Knee OA and twenty-two control subjects participated in the experiment. Considering the asymmetric pressure distribution in feet and the landing habits of Knee OA patients, the plantar surface was split into eight areas, calculating the contact time and maximum force of each area in a gait cycle. Using these characteristics to train, the support vector machine (SVM) reached an accuracy of 93.15%, a precision of 92.39%, and a recall of 92.79%. Furthermore, a prediction model was proposed for the application that aggregates all the results in one test and gives a more accurate result, and the classification accuracy for individuals in the ensemble model is 90.90%. Our technique fills the vacancy of the recognition of patients with Knee OA based on wearable instruments. It provides ideas for intelligent healthcare, which benefits potential Knee OA patients' early diagnosis and treatment.

Technological advances in lower-limb tele-rehabilitation: A review of literature

“… 70 , 73 , 74 , 76 , 77 , 80 , 81 However, it is worth noting that there is still a noticeable lack of work in the area of exercise assessment modules using this technology, as only one study was identified and categorized within this domain. 72 …”

“…Wang et al presented a gait monitoring method to recognize patients with Knee Osteoarthritis (OA) by assessing the plantar pressure signals during walking. 72 Pressure signals were collected by a smart insole embedded with a flexible piezoresistive array (4 × 12 matrix). Their experiment involved 18 participants diagnosed with Knee OA and 22 control participants.…”

“…Wang et al attained an assessment performance of 93.15% using data from pressure insoles with 40 subjects (maximum) and only 1 class (walking). 72 The authors in Anton et al reported a high assessment performance of 95.16%, with a relatively large number of classes (6), and 15 subjects, notably, all were patients. 27…”

Wearable Devices for Gait Measurement - A Case for Textile-Based Devices

“…Insole systems can estimate the distribution of the plantar pressure within a shoe [86] and also possess the capability of rendering a high number of recorded steps, thereby ensuring a long-term recording of gait, both indoors and outdoors [13]. Insole gait measurement systems are available in wide variants based on optoelectronic sensors, accelerometry and gyroscopes force-sensing resistors (FSRs), capacitive sensors, and piezoelectric sensors [87].…”

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

“…Implementing machine learning in wearable devices will allow long-term and dynamic monitoring, which has already been used to detect plantar fasciitis [ 23 ], assess fall risk [ 24 , 25 ] and stroke rehabilitation [ 26 ], and monitor freezing of gait in patients with Parkinson’s disease [ 27 ]. Although Wang et al [ 28 ] used wearable insoles and machine learning to detect KOA, the progression of KOA could not be characterized in the study population, which included patients scheduled for surgery due to ineffectively conservative treatment as well as healthy subjects without symptoms of KOA. Other studies developed machine learning models to classify patients based on K-L grades [ 29 ] and WOMAC scores [ 30 ] using other gait parameters (e.g.…”