Design of a Sensor Insole for Gait Analysis

Ivanov, Kamen; Mei, Zhanyong; Lubich, Ludwig; Guo, Nan; Deng, Xile; Zhao, Zhongwei; Omisore, Olatunji Mumini; Ho, Derek; Wang, Lei

doi:10.1007/978-3-030-27538-9_37

Cited by 15 publications

(15 citation statements)

References 17 publications

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“…The sensor insole used in the present study is designed, implemented, and tested by us. It inherits our previous design described in [9]. The components of the system are shown in Fig.…”

Section: A Hardware Implementation 1) Sensor Insole Prototypementioning

confidence: 99%

“…Recently, sensor-enabled footwear becomes sophisticated and practical enough for daily use. In our previous work [9], we demonstrated a multimodal sensor insole that allows capturing kinetic and kinematic information reflecting the foot dynamic characteristics. Figure 3 illustrates several domains where smart footwear enables mobile and pervasive applications involving health monitoring, remote medical diagnostics, and sports enhancement.…”

Section: Introductionmentioning

confidence: 99%

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Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles

et al. 2020

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Recently, gait attracts attention as a practical biometric for devices that naturally possess walking pattern sensing. In the present study, we explored the feasibility of using a multimodal smart insole for identity recognition. We used sensor insoles designed and implemented by us to collect kinetic and kinematic data from 59 participants that walked outdoors. Then, we evaluated the performance of four neural network architectures, which are a baseline convolutional neural network (CNN), a CNN with a multi-stage feature extractor, a CNN with an extreme learning machine classifier using sensor-level fusion and CNN with extreme learning machine classifier using feature-level fusion. The networks were trained with segmented insole data using 0%, 50%, and 70% segmentation overlap, respectively. For 70% segmentation overlap and both-side data, we obtained mean accuracies of 72.8% ±0.038, 80.9% ±0.036, 80.1% ±0.021 and 93.3% ±0.009, for the four networks, respectively. The results suggest that multimodal sensor-enabled footwear could serve biometric purposes in the next generation of body sensor networks.

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“…The sensor insole used in the present study is designed, implemented, and tested by us. It inherits our previous design described in [9]. The components of the system are shown in Fig.…”

Section: A Hardware Implementation 1) Sensor Insole Prototypementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles

et al. 2020

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“…Nevertheless, a finer resolution in the acquisition of plantar pressures, which spans across the whole footprint, is achieved with sensor distributions presented as follows. Ivanov et al deploys one sensor onto the toe, five sensors onto the metatarsal arch, one sensor onto the lateral arch and two sensors onto the heel [13], as illustrated in Figure 5a. As for another example, Cho et al deploy only three sensors onto the metatarsal arch, three sensors onto the heel and an additional sensor onto the medial arch as an indicator of flat foot [14], as illustrated in Figure 5b.…”

Section: Appl Sci 2021 11 268 4 Of 29mentioning

confidence: 99%

“…The muscular activity for the lower kinetic chain throughout different phases of the gait cycle during the stance phase is listed in Table 1, followed by the muscular activity throughout the swing phase listed in Table 2. (a) (b) (c) (d) Figure 5. Solutions of pressure sensor deployment on the footprint for dynamic assessment: (a) assessment of the footprint medial line with sensors deployed onto the hallux, metatarsal arch, lateral arch and heel respectively [13], (b) additional sensor deployed on the medial arch for flatfoot identification [14], (c) additional sensor deployed on the lesser toes and medial arch for flatfoot identification [15], (d) additional sensor deployed on the lesser toes [16]. In the present work, we have decided upon the assessment of the tibialis anteriorfrom the pretibial muscle group, and the gastrocnemius-from the calf muscle group.…”

Section: Appl Sci 2021 11 268 4 Of 29mentioning

confidence: 99%

A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics

2020

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Walking is the most basic form of human activity for achieving mobility. As an essential function of the human body, the examination of walking is directed towards the assessment of body mechanics in posture and during movement. This work proposes a wearable smart system for the monitoring and objective evaluation of foot biomechanics during gait. The proposed solution assumes the cross-correlation of the plantar pressure with lower-limb muscular activity, throughout the stance phase of walking. Plantar pressure is acquired with an array of resistive pressure sensors deployed onto a shoe insole along the center of gravity progression line. Lower-limb muscular activity is determined from the electromyogram of the tibialis anterior and gastrocnemius lower limb muscles respectively. Under this scenario, physiological gait assumes the interdependency of plantar pressure on the heel area with activation of the tibialis anterior, as well as plantar pressure on the metatarsal arch/toe area with activation of the gastrocnemius. As such, assessment of gait physiology is performed by comparison of a gait map, formulated based on the footprint–lower-limb muscle cross-correlation results, to a reference gait template. A laboratory proof of concept validates the proposed solution in a test scenario which assumes a normal walking and two pathological walking patterns.

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“…It can be extended from a simple pressure balancing solution to a more progressive system which can be used in rehabilitation programs for subjects who are disabled. The shoe-based activity is applied in various fields and targets a wide range of population (Ivanov et al, 2019). They are used to measure the plantar pressure, ground reaction forces, gait analysis.…”

Section: Introductionmentioning

confidence: 99%