Detection of the intention to grasp during reach movements

Abstract

Introduction: Soft-robotic gloves have been developed to enhance grip to support stroke patients during daily life tasks. Studies showed that users perform tasks faster without the glove as compared to with the glove. It was investigated whether it is possible to detect grasp intention earlier than using force sensors to enhance the performance of the glove. Methods: This was studied by distinguishing reach-to-grasp movements from reach movements without the intention to grasp, using minimal inertial sensing and machine learning. Both single-user and multi-user support vector machine classifiers were investigated. Data were gathered during an experiment with healthy subjects, in which they were asked to perform grasp and reach movements. Results: Experimental results show a mean accuracy of 98.2% for single-user and of 91.4% for multi-user classification, both using only two sensors: one on the hand and one on the middle finger. Furthermore, it was found that using only 40% of the trial length, an accuracy of 85.3% was achieved, which would allow for an earlier prediction of grasp during the reach movement by 1200 ms. Conclusions: Based on these promising results, further research will be done to investigate the possibility to use classification of the movements in stroke patients.

Offering a helping hand

“…The data were divided into 200 trials per subject, each trial containing a reach or grasp movement. During a reach movement, the beginning and end of a trial were determined using a threshold detector algorithm for the angular velocity (threshold ± 0.1 rad/s) [181]. In case of a grasp movement, the end of a trial is defined as the moment the pressure sensors in the SEM Glove™ detected contact.…”

“…As described by De Vries et al [181], two different grasp tasks and two different reach tasks towards the five different locations were performed: 1) grasping a wooden ball with a diameter of 7.5 cm; 2) grasping a cylindrical object with a diameter of 6 cm; 3) reaching towards a target location whilst in pronation; and 4) reaching towards a target location whilst in supination.…”

“…The protocol allowed compensatory movements to ensure that natural movements from each individual were captured. In healthy subjects, a minimum number of 70 movements were needed to train the classifier accurately [181]. Based on the results from De Vries et al [181], it was decided to repeat all tasks 5 times per location.…”

“…In healthy subjects, a minimum number of 70 movements were needed to train the classifier accurately [181]. Based on the results from De Vries et al [181], it was decided to repeat all tasks 5 times per location. Besides a specific task and location, the patient was also instructed to have one of two different starting hand postures; either flat on the table with the dorsal side upwards or making a fist with the medial side resting on the table.…”

“…To classify the final hand posture, bend sensors, pressure sensors, position of the fingertips, electromyography, electromagnetic sensors in combination with electromyography, and segment angles were used. In contrast to classifying different final hand postures, De Vries et al [181] studied the possibility to distinguish reach from grasp movements while using a minimal amount of inertial sensing. In that study reach and grasp movements were classified in healthy subjects using single-and multi-user classification support vector machine (SVM) classifiers with accuracies up to 98.2% and 91.4% respectively.…”