Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment

Nelson, Alexander; Waller, Sandy McCombe; Robucci, Ryan; Patel, Chintan; Banerjee, Nilanjan

doi:10.1177/2055668318762063

Cited by 11 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…HGR models are human-computer systems that determine what gesture was performed and when a person performed the gesture. Currently, these systems are used, for example, in several applications, such as intelligent prostheses [1][2][3], sign language recognition [4,5], rehabilitation devices [6,7], and device control [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Jaramillo-Yánez

Benalcázar

Mena-Maldonado

2020

Sensors

169

View full text Add to dashboard Cite

Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Jaramillo-Yánez

Benalcázar

Mena-Maldonado

2020

Sensors

169

View full text Add to dashboard Cite

show abstract

“…However, cameras can lead to privacy concerns for users, especially in residential environments, which limits their applications. Gesture recognition has become a common interaction pattern in recent years with LiDAR, radar, infrared, resistive, and capacitive sensors [5]- [7], [14]- [18]. CapBand [7] is developed to recognize static hand gestures using capacitive sensors on a wrist band with a convolutional neural network (CNN).…”

Section: A Sensors and Pattern Recognitionmentioning

confidence: 99%

An FPGA-Based Upper-Limb Rehabilitation Device for Gesture Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks

Liu¹,

Panahi²,

Andrews³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Upper-Extremity motor impairment affects millions of Americans due to cerebrovascular incidents, spinal cord injuries, or brain trauma. Current therapy practices used to assist these individuals in regaining motor functionality often require extensive time at rehabilitation facilities with potentially prohibitive travel or financial costs. This work presents a mobile low-cost field programmable gate array (FPGA)-smart rehabilitation system that can be used in home environments. The prototype is a rehabilitation table instrumented with a capacitive sensor array (CSA) to track upper-extremity motions of the user through proximity or touch. In addition, inertial measurement units (IMUs) are placed on the affected upper limb and combined with the CSA data with our sensor fusion signal processing architecture. Motions are classified and evaluated using multi-task convolutional recurrent neural networks with three additional motion quality output classes to personalize recognition based on the particular motor skills of each patient. The prototype achieves above 99% accuracy with 32-bit fixed-point format implementation for recognizing dynamic motions and identifying unnatural characteristics (i.e., tremor or limited flexion and extension) in upper limb motions based on sensor values. The convolutional recurrent neural network (C-RNN) fusion classification network is implemented on a 200 MHz Zynq ZCU104 FPGA using an HLS-based design optimized with pipelining and parallelism techniques and achieves 5.4x speedup compared to ARM® Cortex-A53 implementation running at an operating frequency of 1.3 GHz. The prototype is also demonstrated to perform the machine learning classification in real-time.

show abstract

“…Ye et al [101] used c to replace c in the above equation for eliminating the calculation error caused by the influence of electrode position, which can lead to the differences among the initial capacitances. Nelson et al [99], [100] proposed the two gesture recognition algorithms, DTW and an algorithm combined with HMM and DTW, to recognize the EdgeWrite gestures which are alphanumeric gestures. Endres et al [102] used DTW for recognition of 2D Microgestures for drivers.…”

Section: ) Gesture Recognition and Posture Trackingmentioning

confidence: 99%

A Review on Applications of Capacitive Displacement Sensing for Capacitive Proximity Sensor

et al. 2020

View full text Add to dashboard Cite

Capacitive proximity sensors (CPSs) are ubiquitous because of their simple design, low cost and low consumption. Capacitive displacement sensing, as one of the three sensing modalities, works for long distance and can be unitized to measure more physical quantities compared with capacitive volume and deformation sensing. In this paper, we firstly introduce the concept of capacitive displacement sensing. After that, we present applications of capacitive displacement sensing under three broad categories: distance measurements, indirect measurements, and the applications applied in smart environments. Finally, we discuss the challenges and possible solutions for CPSs development. We show that both the detection range and accuracy of CPS can be improved by multi-sensor fusion, and the application scenarios can be extensive through machine/deep learning approaches. We aim to provide a comprehensive, and state-of-theart review of the capacitive displacement sensing, and inspire more researchers and developers to find wide application perspectives.INDEX TERMS Capacitive proximity sensor (CPS), capacitive displacement sensing, distance measurement, indirect measurement, smart environment.

show abstract

Evaluating touchless capacitive gesture recognition as an assistive device for upper extremity mobility impairment

Cited by 11 publications

References 31 publications

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

An FPGA-Based Upper-Limb Rehabilitation Device for Gesture Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks

A Review on Applications of Capacitive Displacement Sensing for Capacitive Proximity Sensor

Contact Info

Product

Resources

About