A Portable Insole Pressure Mapping System

Tee, Kian Sek; Javahar, Yogindra Syam Hari; Saim, Hashim; Zakaria, Wan Nurshazwani Wan; Khialdin, Safinaz Binti Mohd; Isa, Hazlita Mohd; Awad, Mohammed I.; Soon, Chin Fhong

doi:10.12928/telkomnika.v15i4.7227

Cited by 8 publications

(2 citation statements)

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“…Four FSRs were used in each insole design. These sensors were placed on the heel, medial forefoot (MF), lateral forefoot (LF), and toe area of the foot by examining many studies in the literature [28][29][30][31][32]. FSRs transmit their outputs via the analog interface to the development board, which processes the incoming FSR outputs and transfers the master device via Wi-Fi connectivity.…”

Section: Methodsmentioning

confidence: 99%

Activity Recognition Using Different Sensor Modalities and Deep Learning

Ascioglu,

Senol

2023

Applied Sciences

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In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories.

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Section: Methodsmentioning

confidence: 99%

Activity Recognition Using Different Sensor Modalities and Deep Learning

Ascioglu,

Senol

2023

Applied Sciences

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“…The majority of these devices included numerous sensing locations and a wireless data transmission protocol. Force-sensitive resistors or piezoresistive materials were often utilized to provide the flexibility of the sensors [30,31].…”

Section: Introductionmentioning

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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