Identification of Walking Strategies of People With Osteoarthritis of the Knee Using Insole Pressure Sensors

Muñoz-Organero, Mario; Littlewood, Chris; Parker, J. C.; Powell, Lauren; Grindell, Cheryl; Mawson, Sue

doi:10.1109/jsen.2017.2696303

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…In [19], Anzai et al demonstrated that the Knee OA group had a smaller single support phase, greater stance phase time, smaller great toe peak pressure and greater medical midfoot peak pressure than controls. Also in [20], Munoz-Organero found the weight dynamic reallocation at the midfoot and the major asymmetries between pressure patterns in both feet of Knee OA patients. In [21], T. Liikavainio et al found that even in asymptomatic patients with early-stage Knee OA, alterations in plantar loading could be observed during walking.…”

Section: Introductionmentioning

confidence: 90%

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

Wang

Fan

et al. 2022

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 90%

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

Wang

Fan

et al. 2022

IEEE Access

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“…Another example is applications to stroke. In the system proposed by Howell et al, they used 32 FSRs to detect peak ground reaction pressure, which enabled the calculation of the swing and stance phases of the gait cycle [ 79 ]. As stroke patients usually have a shorter stance phase [ 80 , 81 ], this system can be applied to the monitoring of patient rehabilitation.…”

Section: Comparison and Review Of Piezoresistive And Piezoelectric Ps...mentioning

confidence: 99%

Comparison between Piezoelectric and Piezoresistive Wearable Gait Monitoring Techniques

Zhang

Chen

et al. 2022

Materials

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Insole plantar stress detection (PSD) techniques play an important role in gait monitoring. Among the various insole PSD methods, piezoelectric- and piezoresistive-based architectures are broadly used in medical scenes. Each year, a growing number of new research outcomes are reported. Hence, a deep understanding of these two kinds of insole PSD sensors and state-of-the-art work would strongly benefit the researchers in this highly interdisciplinary field. In this context, this review article is composed of the following aspects. First, the mechanisms of the two techniques and corresponding comparisons are explained and discussed. Second, advanced materials which could enhance the performance of current piezoelectric and piezoresistive insole prototypes are introduced. Third, suggestions for designing insole PSD prototypes/products for different diseases are offered. Last, the current challenge and potential future trends are provided.

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“…To monitor and analyze the motion abnormalities of KOA patients, critical indicators include plantar pressure distribution [94], CoP path [98], duration of gait phases, and knee adduction moment (KAM) [93,99]. As listed in Table IV [95,97,98], KOA patients tend to have a more dispersed plantar pressure distribution pattern, longer single support phase duration, smaller CoP path range, and higher KAM value during walking.…”

Section: E Knee Osteoarthritismentioning

confidence: 99%

“…This is because patients' limited knee movement angles will lead to a shorter anteroposterior displacement of CoP. M. M. Organero et al [95] used an insole system with 8 FSR sensors to analyze the walking strategy of KOA patients. Through experiments with 14 KOA patients and 14 healthy patients as the standard, the double support phase duration and forefoot pressure transfer mode (e.g., move speed and route) are found to have a close relationship with KOA.…”

Section: E Knee Osteoarthritismentioning

confidence: 99%

Insole Systems for Disease Diagnosis and Rehabilitation

Gao¹,

Zhang²,

Wang³

et al. 2022

Preprint

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Neurological and orthopedic chronic diseases are proven to numerous complications and related dysfunction for patients, and are leading factors of death and disability. Conventional methods of diagnosis and rehabilitation are conducted at hospitals. However, due to the aging society and controversial privacy protection, traditional methods implemented in medical institutions have faced numerous challenges. Hence, internet of healthcare things (IoHT) based techniques have recently begun to be implemented. In order to provide flexible utilization and secure privacy for patients, wearable devices, like whole body devices based on electromyography (EMG) and radio frequency identification (RFID) insole systems, based on gait-analysis were applied. Among these techniques, insole systems for medical analysis are more suitable, because the root cause of neurologic and orthopedic chronic diseases are directly related to several gait characteristics. Neurologic and orthopedic chronic diseases are caused by lesions of the central nervous system (CNS), peripheral nerves (PN) and orthopedic limbs, similarly gait features are comprehensive results generated by the coordination of CNS, PN and lower limbs However, owing to lack of standards for instrumentation and the knowledge gap between engineering and pathogenic mechanism, the medical application of insole systems and the development of IoHT are hindered. This article is presented to address these issues. In this context, five plantar sensing mechanisms and their related products will be overviewed. Next, pathogenic mechanisms and pathological gait features of six representative chronic neurologic diseases will be displayed. Likewise, the development of corresponding insole systems for supervision and recovery for these diseases will be reviewed as well. Finally, challenges and future trends for current work will be discussed. This article will not only provide guidelines for the design of insole systems targeted for specific diseases but will also inspire the innovation of novel ideas. <br>

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Identification of Walking Strategies of People With Osteoarthritis of the Knee Using Insole Pressure Sensors

Cited by 20 publications

References 32 publications

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

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

Comparison between Piezoelectric and Piezoresistive Wearable Gait Monitoring Techniques

Insole Systems for Disease Diagnosis and Rehabilitation

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