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Abstract

Intensive and adaptive rehabilitation therapy is beneficial for post-stroke recovery. Three modes of rehabilitation are generally performed at different stages after stroke: external force-based control in the acute stage, assistive force-based rehabilitation in the midway of recovery and resistive force-based rehabilitation in the last stage. To achieve the above requirements, an innovative elbow exoskeleton has been developed to incorporate the three modes of rehabilitation in a single structure. The structure of the exoskeleton has been designed in such a way that the whole working region is divided into three where each region can provide a different mode of rehabilitation. Recovery rate can be varied for individuals since it depends on various parameters. To evaluate the rate of recovery, three joint parameters have been identified: range of angular movement, angular velocity and joint torque. These parameters are incorporated into the framework of planning a novel rehabilitation strategy, which is discussed in this article along with the structural description of the designed exoskeleton.

Keywords

Rehabilitation devices mechanism design exoskeleton post-stroke recovery mechanical design

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References

Thrift

Thayabaranathan

Howard

et al . Global stroke statistics. Int J Stroke 2017; 12: 13–32.

Xie

SQ.

Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Med Eng Phys 2012; 34: 261–268.

Krebs

Ferraro

Buerger

et al . Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus. J NeuroEng Rehabil 2004; 1: 5.

Proietti

Crocher

Roby-Brami

et al . Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies. IEEE Rev Biomed Eng 2016; 9: 4–14.

Pineda-Rico

de Lucio

JAS

Martinez Lopez

et al . Design of an exoskeleton for upper limb robot-assisted rehabilitation based on co-simulation. J Vibroeng 2016; 18: 3269–3278.

Chonnaparamutt

Supsi

SEFRE: semiexoskeleton rehabilitation system. Appl Bionic Biomech 2016; 2016: 8306765.

Peternel

Noda

Petrič

et al . Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS ONE 2016; 11: e0148942.

Bhadane

Liu

Rymer

et al . Re-evaluation of EMG-torque relation in chronic stroke using linear electrode array EMG recordings. Sci Rep 2016; 6: 28957.

Cesqui

Tropea

Micera

et al . EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study. J NeuroEng Rehabil 2013; 10: 75.

10.

Koyas

Hocaoglu

Patoglu

et al . Detection of intention level in response to task difficulty from EEG signals. In: International workshop on machine learning for signal processing, Southampton, 22–25 September 2013, pp.1–6. New York: IEEE.

11.

Marchal-Crespo

Reinkensmeyer

DJ.

Review of control strategies for robotic movement training after neurologic injury. J NeuroEng Rehabil 2009; 6: 20.

12.

Ibrahim

Berger

Trippel

et al . Stretch-induced electromyographic activity and torque in spastic elbow muscles: differential modulation of reflex activity in passive and active motor tasks. Brain 1993; 116: 971–989.

13.

Chiri

Giovacchini

Vitiello

et al . HANDEXOS: towards an exoskeleton device for the rehabilitation of the hand. In: IEEE/RSJ international conference on intelligent robots and systems, St. Louis, MO, 10–15 October 2009, pp.1106–1111. New York: IEEE.

14.

Herder

Vrijlandt

Antonides

et al . Principle and design of a mobile arm support for people with muscular weakness. J Rehabil Res Dev 2006; 43: 591–604.

15.

Manna

Dubey

VN.

Upper arm exoskeleton – what specifications will meet users’ acceptability? In: Fisher

(ed.) Robotics: new research. New York: Nova Science Publishers, 2016, pp.123–69.

16.