Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities

Manna, Soumya K.; Azhar, M. A. Hannan Bin; Greace, Ann

doi:10.1016/j.heliyon.2023.e15210

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…Pressure sensors measure the applied pressure in the sensing area, and force resistive sensors (FSRs) are commonly used to detect physical tension, squeezing, and contact. Examples include detecting gait patterns with FSR sensors embedded in shoes [43], gait abnormalities [44], and monitoring of daily life among stroke survivors [45]. An FSR can monitor multiple major muscle groups that govern the movements of the hand and wrist [46].…”

Section: ) Pressure Sensorsmentioning

confidence: 99%

A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

Sengupta,

Rao,

Yan

et al. 2024

IEEE Access

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Stroke is one of the leading causes of disability among the elderly population and is a significant public health problem worldwide. The main impact of stroke is functional disabilities due to motor impairment after stroke. Advances in modern medicine and technology have significantly improved diagnosis and treatment; however, most post-stroke care is based on the effectiveness of rehabilitation. Stroke rehabilitation depends on two main components: (i) training (or therapy) to restore the patient to pre-stroke mobility and (ii) assessing motor functionality of affected patients performing activities to track motor recovery. This article highlights how combining wearable devices and machine learning (ML) produces new pathways for effective stroke rehabilitation. While wearable devices help capture patient movements at much finer time resolutions, ML allows us to build predictive models from wearable data to assist clinicians in diagnosis and treatments. Specifically, we expand on how wearable devices and ML can improve monitoring quality in training intervention, assessment, and remote monitoring. In addition, we provide our main findings from the literature, research challenges, and future directions in post-stroke therapies using wearable devices and ML.INDEX TERMS Stroke rehabilitation, wearable devices, machine learning, interventions, remote monitoring.

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Section: ) Pressure Sensorsmentioning

confidence: 99%

A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

Sengupta,

Rao,

Yan

et al. 2024

IEEE Access

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“…Similarly, the other face of the sensors was covered with a 0.6 mm thick 3D printed-PLA sheet, which provided the FSR sensors with a solid reactive surface for accurate data acquisition. All sensors were connected to the microcontroller board (Teensy 4.1 Development Board, PJRC.COM, LLC., Sherwood, OR, USA) through a voltage divider circuit [43,44]. A 20-band flat ribbon cable (FRC) was used to connect the sensors in the insole to the electronics hardware.…”

Section: Plantar Pressure-measuring Insolementioning

confidence: 99%

Development of a Novel Customized Insole for Effective Pressure Offloading in Diabetic Patients

Bose,

Singh,

Gupta

et al. 2024

Prosthesis

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The number of people with diabetes is rising day-by-day, which also raises the incidence of diabetic ulcers, sensation loss in the foot’s plantar area, and in extreme instances, amputations. Using customized shoes, unloading orthoses, insoles, and other strategies may help control these issues to some degree. In this work, a novel modular diabetic insole was designed and fabricated to effectively offload the abnormal or peak plantar pressures in diabetic patients. The pressure values in the plantar region were quantified using an in-house-developed plantar pressure-measuring insole consisting of force sensitive resistor (FSR) sensors. The effectiveness of the modular diabetic insole was tested qualitatively and quantitatively. The qualitative performance of the insole was reported using Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) in terms of subjective parameters like comfort, ease of use, effectiveness, etc. and calculated as 4.7 ± 0.18. Thereafter, the wearable pressure-measuring insole was used to investigate the feasibility of modular insole for the plantar pressure offloading during standing and walking conditions. It was observed that the maximum average zonal pressure (AZP) was reduced by up to 99% from 121.30 ± 3.72 kPa to 0.22 ± 0.18 kPa for the standing condition whereas it was reduced to 6.76 ± 2.03 kPa from 197.71 ± 3.21 kPa with a percentage value of 96% for the walking condition. In conclusion, the findings of this work validate the effectiveness of the modular diabetic insole as an intervention tool for diabetic foot ulcer prevention.

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Wearable Platform for Tennis Players Performance Monitoring: a Proof of Concept

Bortolotti,

Coviello,

Giannattasio

et al. 2023

2023 IEEE International Workshop on Sport, Technology and Research (STAR)

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Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities

Cited by 3 publications

References 60 publications

A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

Development of a Novel Customized Insole for Effective Pressure Offloading in Diabetic Patients

Wearable Platform for Tennis Players Performance Monitoring: a Proof of Concept

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