Development of a Bendable Outsole Biaxial Ground Reaction Force Measurement System

Park, Junghoon; Kim, Sangjoon Jonathan; Na, Youngjin; Kim, Yeongjin; Kim, Jung

doi:10.3390/s19112641

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…In recent years, wearable sensors have attracted attention in the field of human biomechanics, and some of these devices measure the GRF during walking and running motion [ 98 , 99 ]. Such devices for measuring the multi-axis GRF are composed of compact multi-axis tactile sensors embedded in the insoles [ 100 ] or outsoles [ 101 , 102 ] of shoes. The local GRF distribution, which depends on the number of tactile sensors, can be measured when a target human walks with the shoes on.…”

Section: Measurement Of the Grf/gafmentioning

confidence: 99%

MEMS-Based Micro Sensors for Measuring the Tiny Forces Acting on Insects

Takahashi

2022

Sensors

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Small insects perform agile locomotion, such as running, jumping, and flying. Recently, many robots, inspired by such insect performance, have been developed and are expected to be smaller and more maneuverable than conventional robots. For the development of insect-inspired robots, understanding the mechanical dynamics of the target insect is important. However, evaluating the dynamics via conventional commercialized force sensors is difficult because the exerted force and insect itself are tiny in strength and size. Here, we review force sensor devices, especially fabricated for measuring the tiny forces acting on insects during locomotion. As the force sensor, micro-force plates for measuring the ground reaction force and micro-force probes for measuring the flying force have mainly been developed. In addition, many such sensors have been fabricated via a microelectromechanical system (MEMS) process, due to the process precision and high sensitivity. In this review, we focus on the sensing principle, design guide, fabrication process, and measurement method of each sensor, as well as the technical challenges in each method. Finally, the common process flow of the development of specialized MEMS sensors is briefly discussed.

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Section: Measurement Of the Grf/gafmentioning

confidence: 99%

MEMS-Based Micro Sensors for Measuring the Tiny Forces Acting on Insects

Takahashi

2022

Sensors

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“…As shown in Fig. 14(b), the sensor prototype showed a maximum hysteresis of 4.7%FS for the cyclic shear loading with an amplitude of 15.9 N and 5.8%FS under normal loading (amplitude 105 N), slightly higher than the hysteresis error of the soft sensor presented in [49] (4.3% FS for normal loading) and [50] (3.2% for normal loading and 4.0% for shear loading). These hysteresis properties can be altered and optimised through modification of the compressible layer, for example using different elastomeric materials.…”

Section: F Sensor Evaluation For Force Measurementmentioning

confidence: 84%

An Inductive Force Sensor for In-Shoe Plantar Normal and Shear Load Measurement

et al. 2020

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Diabetic foot ulcers (DFUs) are a severe global public health issue. Plantar normal and shear load are believed to play an important role in the development of foot ulcers and could be a valuable indicator to improve assessment of DFUs. However, despite their promise, plantar load measurements currently have limited clinical application, primarily due to the lack of reliable measurement techniques particularly for shear load measurements. In this paper we report on the design and evaluation of a novel tri-axis force sensor to measure both normal and shear load on the foot's plantar surface simultaneously. The sensor consists of a group of inductive sensing coils above which a conductive target is placed on a hyperelastic elastomer. Movement of the target under load affects the coil inductances which are measured and digitized by an embedded system. Using a computational finite element model, we investigated the influence of sensing coil form and configuration on sensor performance. A sensor configured with four-square coils and maximal turns provided the best performance for plantar load measurements. A prototype was fabricated and calibrated using a neural network to map the non-linear relationship between the sensor output and the applied tri-axis load. Experimental evaluation indicates that the tri-axis sensor can effectively detect shear load of ±16 N and normal load up to 105 N (RMS errors: 1.05 N and 1.73 N respectively) with a high performance. Overall, this sensor provides a promising basis for plantar normal and shear load measurement which are crucial for improved assessment of DFU.

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“…Multiple technology options exist for kinetic data measurement including low-cost smart insoles, Nintendo Wii Balance Board, and highend gold standard force plates for kinetic data, that can be used to derive ground reaction forces (GRFs), rate of force development (RFD) and centre of pressure (CoP) parameters. In sports performance laboratories, the force plates are routinely used [526,759], but recently low-cost insoles [195,250,378,612,795,864] and Nintendo R Wii Balance Board TM [543] have been investigated as alternatives as the cost of force plates can be prohibitive in some cases. Recently, nonlearning mathematical [863], and deep learning models have been developed to approximate the forces generated by the athlete, solely from nonkinetic measurements, such as from kinematic data [323,375,724].…”

Section: B Kinetic: Force Measurementmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Development of a Bendable Outsole Biaxial Ground Reaction Force Measurement System

Cited by 13 publications

References 36 publications

MEMS-Based Micro Sensors for Measuring the Tiny Forces Acting on Insects

MEMS-Based Micro Sensors for Measuring the Tiny Forces Acting on Insects

An Inductive Force Sensor for In-Shoe Plantar Normal and Shear Load Measurement

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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