A Wearable Sensing and Training System: Towards Gait Rehabilitation for Elderly Patients With Knee Osteoarthritis

He, Zexia; Liu, Tao; Yi, Jingang

doi:10.1109/jsen.2019.2908417

Cited by 26 publications

(23 citation statements)

References 28 publications

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“…The duplicate removal rendered 376 articles, which were screened at the title/abstract level. A full-text screening process of 107 articles resulted in 72 articles, and these 72 articles were included in our scoping review [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ...…”

Section: Resultsmentioning

confidence: 99%

“…Previous research has demonstrated the ability of inertial sensors to estimate joint moments, but this has most often occurred in healthy populations [ 101 , 102 , 103 ]. Alternatively, we identified three studies which examined KAM [ 41 , 83 , 86 ], as well as one examining joint contact forces [ 36 ] and one examining ankle joint moments [ 70 ]. The first proof of principle study was conducted by van den Noort et al (2013 [ 83 ]), which used eight inertial sensors on the lower limbs, in combination with instrumented force shoes, to estimate KAM.…”

Section: Discussionmentioning

confidence: 99%

“…While successful at the group level, significant errors remained on an individual level which related to the estimation of joint centres, sensory orientation/alignment, and inaccuracies in force measurements. More recently, Wang et al (2020 [ 86 ]) and He and Liu (2019 [ 41 ]) evaluated the use of patient-specific inertial sensor systems which involved only one sensor per ankle to estimate KAM in real-time. The inertial sensor applications required a short calibration using data from a conventional system before a patient-specific model could be created to track real-time changes in KAM using only inertial sensors.…”

Section: Discussionmentioning

confidence: 99%

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Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Kobsar

Masood

Khan

et al. 2020

Sensors

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Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Kobsar

Masood

Khan

et al. 2020

Sensors

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“…Zexia He et al designed a wearable sensing and training system using a motion sensor and six pressure-sensitive electric conductive rubber sensors. This system helped the elder with knee osteoarthritis to estimate their adduction moment for rehabilitation assessment (He et al, 2019). Schlachetzki et al developed a wearable sensors-based gait analysis system with a high biomechanical resolution for gait impairment in PD.…”

Section: Using Wearable Sensorsmentioning

confidence: 99%

Wearable Devices for Gait Analysis in Intelligent Healthcare

Liu

Zhao

Zheng

et al. 2021

Front. Comput. Sci.

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In this study, we review the role of wearable devices in tracking our daily locomotion. We discuss types of wearable devices that can be used, methods for gait analyses, and multiple healthcare-related applications aided by artificial intelligence. Impaired walking and locomotion are common resulting from injuries, degenerative pathologies, musculoskeletal disorders, and various neurological damages. Daily tracking and gait analysis are convenient and efficient approaches for monitoring human walking, where concreate and rich data can be obtained for examining our posture control mechanism during body movement and providing enhanced clinical pieces of evidence for diagnoses and treatments. Many sensors in wearable devices can help to record data of walking and running; spatiotemporal and kinematic variables can be further calculated in gait analysis. We report our previous works in gait analysis, discussing applications of wearable devices for detecting foot and ankle lesions, supporting surgeons in early diagnosis, and helping physicians with rehabilitation.

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“…Those algorithms are classified as model- or nonmodel-based strategies. The focus of model-based algorithms is to put on the estimation of joint torques by forward or inverse dynamics [ 13 , 14 ]. For the forward dynamics of the human neuromusculoskeletal system, most existing models are based on Hill’s work.…”

Section: Introductionmentioning

confidence: 99%

Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton

Yang

Yin

2020

Sensors

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Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r2 values were higher than those of GP.

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A Wearable Sensing and Training System: Towards Gait Rehabilitation for Elderly Patients With Knee Osteoarthritis

Cited by 26 publications

References 28 publications

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Wearable Devices for Gait Analysis in Intelligent Healthcare

Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton

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