Investigating the Effect of Vibrotactile Feedback in Transfemoral Amputee With and Without Movable Ankle Joint

Vimal, Amit Kumar; Verma, Vinay Kumar; Khanna, Nitin; Joshi, Deepak

doi:10.1109/tnsre.2020.3035833

Cited by 10 publications

(5 citation statements)

References 38 publications

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“…Recent advancements in the development of wearable feedback systems that incorporates vibrotactile, electro tactile, or auditory sensory clue mapped to the specific spatial and temporal gait parameters for amputee's gait training facilitates them with enjoyable and non-obtrusive long-term training environment [62], [63]. In the future, the visual feedback system may be carefully replaced with the vibrotactile sensory clue through one-to-one mapping of the CoP spatial displacement to the different spatial location and/or varying intensity of the vibrotactile motors similar to our previous works [16], [64]. This strategy may envision the incorporation of posterior CoP shift feedback strategy in a wearable device inbuilt into the prostheses that can seamlessly provide real-time feedback training outside the laboratory environment.…”

Section: D) Retentionmentioning

confidence: 99%

“…Here, our goal is to design and implement an intuitive biofeedback environment that is capable of Design and validation of a real-time visual feedback system to improve minimum toe clearance (mTC) in transfemoral amputees T enhancing the toe clearance in transfemoral amputee in a much conducive manner. In the past, motor learning during biofeedback training has shown a positive outcome during gait and balance performance [16], [17]. Visual feedback during gait training has shown a promising effect on various gait mobility outcomes, with significant research focusing on the gait symmetry improvements and balance recovery in amputees and healthy individuals [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

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Design and Validation of a Real-Time Visual Feedback System to Improve Minimum Toe Clearance (mTC) in Transfemoral Amputees

Tiwari

Joshi

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Tripping is accompanied by reduced minimum toe clearance (mTC) during the swing phase of gait. The risk of fall due to tripping among transfemoral amputees is nearly 67% which is greater than the transtibial amputees. Therefore, intervention to improve mTC can potentially enhance the quality of life among transfemoral amputees. In this paper, we first develop a real-time visual feedback system with center of pressure (CoP) information. Next, we recruited six able-bodied and three transfemoral amputees to investigate the effect on mTC while participants were trained to shift the CoP anteriorly/posteriorly during heel strike. Finally, to assess the lasting effect of training on mTC, retention trials were conducted without feedback. During feedback, posterior shift in the CoP improved the mTC significantly from 4.68±0.40cm to 6.12±0.68cm (p<0.025) in able-bodied participants. A similar significant improvement in mTC from 4.60±0.55cm to 5.62±0.57cm was observed in amputees during posterior shift of CoP. Besides mTC, maximal toe clearances, i.e., maxTC1 and maxTC2, also showed a significant increase (p<0.025) during the posterior shift of CoP in both the participants. Moreover, during retention, mTC did not differ significantly (p>0.05) from feedback condition in amputee, suggesting a positive effect of feedback training. The foot-to-ground angle (FGA) at mTC increased significantly (P<0.025) during posterior shift feedback in able-bodied suggests active ankle dorsiflexion in increasing mTC. However, in amputees, FGA at mTC did not differ significantly during both anterior and posterior CoP shift feedback. The present findings suggest CoP feedback as a potential strategy during gait rehabilitation of transfemoral amputees.

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Section: D) Retentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Validation of a Real-Time Visual Feedback System to Improve Minimum Toe Clearance (mTC) in Transfemoral Amputees

Tiwari

Joshi

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The NPs (number of peaks) and ZCs (zero crossings) of a stochastic process can be calculated using their spectral moments [26], [14]. This is defined in equations ( 7) and (8).…”

Section: Proposed Dlpr Framework For Emg-prmentioning

confidence: 99%

A Robust and Accurate Deep Learning based Pattern Recognition Framework for Upper Limb Prosthesis using sEMG

Pancholi,

Joshi,

Joshi

2021

Preprint

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In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets. Moreover, the performance of traditional machine learning-based methods show limitation to categorize over a certain number of classes and degrades over a period of time. In this paper, an accurate, robust, and fast convolutional neural network-based framework for EMG pattern identification is presented. To assess the performance of the proposed system, five publicly available and benchmark data-sets of upper limb activities were used. This data-set contains 49 to 52 upper limb motions (NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and data with arm position variation for intact and amputated subjects. The classification accuracies of 91.11% (53 classes), 89.45% (49 classes), 81.67% (49 classes of amputees), 95.67% (6 classes with force variation), and 99.11% (8 classes with arm position variation) have been observed during the testing and validation. The performance of the proposed system is compared with the state of art techniques in the literature. The findings demonstrate that classification accuracy and time complexity have improved significantly. For signal preprocessing and deep learning techniques, Keras which is a highlevel API for TensorFlow to build profound learning models has been utilized. The proposed method has been tested on the Intel Core i7 3.5GHz, 7th Gen CPU with 8GB DDR4 RAM.

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“…Participants responded to the questionnaire items (Table I) on the Likert scale (1 to 5) for strongly disagree to strongly agree. The Likert scale was used to evaluate the quantification of the acceptability/usability of the WFS using a score adapted from the previous studies [36], [37].…”

Section: Experimental Protocol 1 Wfs Familiarization Sessionmentioning

confidence: 99%

Wearable Haptic Feedback System for Training Propulsion Force in Individuals with Solid Ankle Foot Orthosis: Development and Validation

Tiwari¹

2022

Preprint

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Investigating the Effect of Vibrotactile Feedback in Transfemoral Amputee With and Without Movable Ankle Joint

Cited by 10 publications

References 38 publications

Design and Validation of a Real-Time Visual Feedback System to Improve Minimum Toe Clearance (mTC) in Transfemoral Amputees

Design and Validation of a Real-Time Visual Feedback System to Improve Minimum Toe Clearance (mTC) in Transfemoral Amputees

A Robust and Accurate Deep Learning based Pattern Recognition Framework for Upper Limb Prosthesis using sEMG

Wearable Haptic Feedback System for Training Propulsion Force in Individuals with Solid Ankle Foot Orthosis: Development and Validation

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