Effective Multi-Mode Grasping Assistance Control of a Soft Hand Exoskeleton Using Force Myography

Abstract

Human intention detection is fundamental to the control of robotic devices in order to assist humans according to their needs. This paper presents a novel approach for detecting hand motion intention, i.e., rest, open, close, and grasp, and grasping force estimation using force myography (FMG). The output is further used to control a soft hand exoskeleton called an SEM Glove. In this method, two sensor bands constructed using force sensing resistor (FSR) sensors are utilized to detect hand motion states and muscle activities. Upon placing both bands on an arm, the sensors can measure normal forces caused by muscle contraction/relaxation. Afterwards, the sensor data is processed, and hand motions are identified through a threshold-based classification method. The developed method has been tested on human subjects for object-grasping tasks. The results show that the developed method can detect hand motions accurately and to provide assistance w.r.t to the task requirement.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

“…To acquire effective biosignals, the sensor needs to be in close contact with the skin, and piezoresistive sensors have this characteristic. The most frequently used piezoresistive sensors are force-sensitive resistors (FSR), for instance, FSR 402 [ 27 , 28 , 29 ] and FSR 400 [ 16 , 30 ], which are based on resistive polymer thick film sensor (RPTF) technology. Because of their thin profile, flexibility, and low cost, they become a practical solution for prosthetic pressure measurement [ 31 ].…”

“…In time domain, there are five commonly used parameters to describe signal features: mean absolute value (MAV) [ 17 , 82 , 83 ], mean absolute value slope [ 22 , 27 ], slope sign changes (SSC) [ 83 , 84 , 85 ], zero crossing (ZC) [ 83 , 84 ] and waveform length (WL) [ 86 ]. These features mainly apply to signal amplitude, while in the frequency domain, signal power information and frequency information are mainly utilized.…”

“…In consideration of simplifying the procedure, Muhammad chose slope to displace amplitude. They set four thresholds to distinguish four hand motions: rest, open, close, and grasp [ 27 ].…”

High‐Performance Hydrogel Sensors Enabled Multimodal and Accurate Human–Machine Interaction System for Active Rehabilitation

“…[29,30] In addition to EMG signals, forcemyography (FMG) is another important muscle activity signal. [31][32][33][34] To date, various high-sensitivity pressure sensors have been utilized to detect subtle FMG signals by tightly attaching to the skin surface, such as resistive sensors, piezoelectric sensors, resistive polymer thick film sensors, and textile-based sensors, and has also been widely applied in active rehabilitation. [33,[35][36][37][38][39] Among these sensors, resistive pressure sensors are the most commonly used sensors in FMG detection due to their simple detection circuits and higher stability.…”

Feasibility of force myography for the direct control of an assistive robotic hand orthosis

“…Primarily, FMG has been used in gesture classification for prosthetics [7,13,8,14]. Only a limited number of studies have also exploited FMG for RHO control, whereas in most cases the signal was collected from the forearm contralateral to the worn RHO [15,16]. However, measuring FMG from the contralateral side restricts the conduction of bimanual tasks which are crucial for many activities in daily living.…”