Goal orientated stroke rehabilitation utilising electrical stimulation, iterative learning and Microsoft Kinect

Exell, Timothy; Freeman, Christopher; Meadmore, Katie; Kutlu, Mustafa; Rogers, Eric; Hughes, Ann‐Marie; Hallewell, Emma; Burridge, Jane

doi:10.1109/icorr.2013.6650493

Cited by 24 publications

(11 citation statements)

References 21 publications

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“…If required, mechanical support was provided by a SaeboMAS, a dynamic mobile arm support that acts as an unweighting device, facilitating movement by supporting the arm against gravity. FES is mediated by ILC controllers: between each trial, the ILC scheme modifies the FES signal applied to each muscle by using data recorded over the previous trials together with a full dynamic model of the arm, in order to precisely assist performance during the next attempt (see [ 17 ]).…”

Section: Methodsmentioning

confidence: 99%

“…This has been demonstrated using iterative learning control (ILC), which uses data from previous attempts at a task to update the FES control signal on the current attempt. ILC employs a desired ‘reference trajectory’ for each joint angle, together with a biomechanical dynamic model of the arm’s response to FES, in order to adjust the applied stimulation signals so that the error between the patient’s joint angle profiles and the set of reference trajectories is reduced over successive attempts [ 15 - 17 ]. Reduction in error thereby corresponds to improved performance.…”

Section: Introductionmentioning

confidence: 99%

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The application of precisely controlled functional electrical stimulation to the shoulder, elbow and wrist for upper limb stroke rehabilitation: a feasibility study

Meadmore

Exell

Hallewell

et al. 2014

J NeuroEngineering Rehabil

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BackgroundFunctional electrical stimulation (FES) during repetitive practice of everyday tasks can facilitate recovery of upper limb function following stroke. Reduction in impairment is strongly associated with how closely FES assists performance, with advanced iterative learning control (ILC) technology providing precise upper-limb assistance. The aim of this study is to investigate the feasibility of extending ILC technology to control FES of three muscle groups in the upper limb to facilitate functional motor recovery post-stroke.MethodsFive stroke participants with established hemiplegia undertook eighteen intervention sessions, each of one hour duration. During each session FES was applied to the anterior deltoid, triceps, and wrist/finger extensors to assist performance of functional tasks with real-objects, including closing a drawer and pressing a light switch. Advanced model-based ILC controllers used kinematic data from previous attempts at each task to update the FES applied to each muscle on the subsequent trial. This produced stimulation profiles that facilitated accurate completion of each task while encouraging voluntary effort by the participant. Kinematic data were collected using a Microsoft Kinect, and mechanical arm support was provided by a SaeboMAS. Participants completed Fugl-Meyer and Action Research Arm Test clinical assessments pre- and post-intervention, as well as FES-unassisted tasks during each intervention session.ResultsFugl-Meyer and Action Research Arm Test scores both significantly improved from pre- to post-intervention by 4.4 points. Improvements were also found in FES-unassisted performance, and the amount of arm support required to successfully perform the tasks was reduced.ConclusionsThis feasibility study indicates that technology comprising low-cost hardware fused with advanced FES controllers accurately assists upper limb movement and may reduce upper limb impairments following stroke.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The application of precisely controlled functional electrical stimulation to the shoulder, elbow and wrist for upper limb stroke rehabilitation: a feasibility study

Meadmore

Exell

Hallewell

et al. 2014

J NeuroEngineering Rehabil

Self Cite

View full text Add to dashboard Cite

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“…The system is designed for children suffering from motor disabilities and presents the rehabilitation progress to the therapists, as per defined standards. The researches [ 58 , 59 ] exploits the Kinect’s skeleton tracking to analyze the rehabilitation in upper limbs. Chang et al [ 59 ] also validate the tracking results of Kinect sensor using the output of motion capturing system, known as OptiTrack.…”

Section: Related Workmentioning

confidence: 99%

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Khan

Schneider

Farid

et al. 2018

Sensors

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Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

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“…Exell et al [ 48 ] used Kinect's skeletal tracking for an upper limb rehabilitation. They used functional electrical stimulation (FES) to facilitate and rehabilitate patient's arm movement while the patient was also being assisted by a weight-compensating mechanical system.…”

Section: Clinical Evaluation Of Kinectmentioning

confidence: 99%

A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation

Hondori

Khademi

2014

Journal of Medical Engineering

236

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This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson's, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect's accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.

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Goal orientated stroke rehabilitation utilising electrical stimulation, iterative learning and Microsoft Kinect

Cited by 24 publications

References 21 publications

The application of precisely controlled functional electrical stimulation to the shoulder, elbow and wrist for upper limb stroke rehabilitation: a feasibility study

The application of precisely controlled functional electrical stimulation to the shoulder, elbow and wrist for upper limb stroke rehabilitation: a feasibility study

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation

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