[Abstract + References] A Novel Exoskeleton with Fractional Sliding Mode Control for Upper Limb Rehabilitation


The robotic intervention has great potential in the rehabilitation of post-stroke patients to regain their lost mobility. In this paper, firstly, we present a design of a novel, 7 degree-of-freedom (DOF) upper limb robotic exoskeleton (u-Rob) that features shoulder scapulohumeral rhythm with a wide range of motions (ROM) compared to other existing exoskeletons. An ergonomic shoulder mechanism with two passive DOF was included in the proposed exoskeleton to provide scapulohumeral motion with corresponding full ROM. Also, the joints of u-Rob have more range of motions compared to its existing counterparts. Secondly, we propose a fractional sliding mode control (FSMC) to control u-Rob. Applying the Lyapunov theory to the proposed control algorithm, we showed the stability of it. To control u-Rob, FSMC has shown effectiveness to handle unmodeled dynamics (e.g. friction, disturbance, etc.) in terms of better tracking and chatter compared to traditional SMC.


1.Stroke Statistics In, (The Internet Stroke Centre 2019).Google Scholar
2.BenjaminE. J.BlahaM. J.ChiuveS. E.CushmanM.DasS. FerrantiS. D.FloydJ.FornageM.GillespieC.IsasiC. R.JimenezM. C.JordanL. C.JuddS. E.LacklandD.LichtmanJ. H.LisabethL.LiuS.LongeneckerC. T.MackeyR. H.MatsushitaK.MozaffarianD.MussolinoM. E.NasirK.NeumarR. W.PalaniappanL.PandeyD. K.ThiagarajanR. R.ReevesM. J.RitcheyM.RodriguezC. J.RothG. A.RosamondW. D.SassonC.TowfighiA.TsaoC. W.TurnerM. B.ViraniS. S.VoeksJ. H.WilleyJ. Z.WilkinsJ. T.WuJ. H.AlgerH. M.WongS. S.P. Muntner and On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee, “Heart disease and stroke statistics-2017 update: A report from the American Heart Association,” Circulation 135(10), e146e603 (2017).CrossRef | Google Scholar
3.Rehabilitation Therapy after a Stroke In, (National Stroke Association, 2019).Google Scholar
4.PoliP.MoroneG.RosatiG. and MasieroS., “Robotic technologies and rehabilitation: New tools for stroke patients’ therapy,” BioMed Res. Int. 20138 (2013).Google Scholar
5.BaiS.ChristensenS. and IslamM. R. U., “An Upper-body Exoskeleton with a Novel Shoulder Mechanism for Assistive Applications,” 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (2017) pp. 10411046.Google Scholar
6.BrahmiB.SaadM.LunaC. O.ArchambaultP. S. and RahmanM. H., “Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties,” Robotica 36(11), 17571779 (2018).CrossRef | Google Scholar
7.CarignanC.TangJ.RoderickS. and NaylorM., “A Configuration-Space Approach to Controlling a Rehabilitation Arm Exoskeleton,” 2007 IEEE 10th International Conference on Rehabilitation Robotics (2007) pp. 179187.Google Scholar
8.ChristensenS. and BaiS.A Novel Shoulder Mechanism with a Double Parallelogram Linkage for Upper-Body Exoskeletons (Springer International PublishingCham2017) pp. 5156.Google Scholar
9.CuiX.ChenW.JinX. and AgrawalS. K., “Design of a 7-DOF Cable-Driven Arm Exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance,” IEEE/ASME Trans. Mechatron. 22(1), 161172 (2017).CrossRef | Google Scholar
10.KiguchiK.EsakiR.TsurutaT.WatanabeK. and FukudaT., “An exoskeleton system for elbow joint motion rehabilitation,” Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003) (2003) vol. 1222, pp. 12281233.Google Scholar
11.KiguchiK. and HayashiY., “An EMG-based control for an upper-limb power-assist exoskeleton robot,” IEE Trans. Syst. Man Cybernetics, Part B (Cybernetics) 42(4), 10641071 (2012).CrossRef | Google Scholar | PubMed
12.KiguchiK.RahmanM. H.SasakiM. and TeramotoK., “Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist,” Robot. Auton. Syst. 56(8), 678691 (2008).CrossRef | Google Scholar
13.KimB. and DeshpandeA. D., “Controls for the Shoulder Mechanism of an Upper-body Exoskeleton for Promoting Scapulohumeral Rhythm,” 2015 IEEE International Conference on Rehabilitation Robotics (ICORR) (2015) pp. 538542.Google Scholar
14.KimB. and DeshpandeA. D., “An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation,” Int. J. Rob. Res. 36(4), 414435 (2017).CrossRef | Google Scholar
15.LiuL.ShiY.-Y. and XieL., “A novel multi-dof exoskeleton robot for upper limb rehabilitation,” J. Mech. Med. Biol. 16(08), 1640023 (2016).CrossRef | Google Scholar
16.MahdavianM.ToudeshkiA. G. and Yousefi-KomaA., “Design and Fabrication of a 3DoF Upper Limb Exoskeleton,” 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM) (2015) pp. 342346.Google Scholar
17.MiheljM.NefT. and RienerR., “ARMin II – 7 DoF Rehabilitation Robot: Mechanics and Kinematics,” Proceedings 2007 IEEE International Conference on Robotics and Automation (2007) pp. 41204125.Google Scholar
18.NefT.GuidaliM.Klamroth-MarganskaV. and RienerR., “ARMin – Exoskeleton Robot for Stroke Rehabilitation,” In: World Congress on Medical Physics and Biomedical Engineering (DösselO. and SchlegelW. C., eds.) September 7–12, 2009, Munich, Germany (Springer Berlin HeidelbergBerlin, Heidelberg, 2009) pp. 127130.Google Scholar
19.NefT.GuidaliM. and RienerR., “ARMin III – Arm therapy exoskeleton with an ergonomic shoulder actuation,” Appl. Bionics Biomech. 6(2), (2009) pp. 127142.CrossRef | Google Scholar
20.NefT.MiheljM.KieferG.PerndlC.MullerR. and RienerR., “ARMin – Exoskeleton for Arm Therapy in Stroke Patients,” 2007 IEEE 10th International Conference on Rehabilitation Robotics (2007) pp. 6874.Google Scholar
21.NefT.MiheljM. and RienerR., “ARMin: A robot for patient-cooperative arm therapy,” Med. Biol. Eng. Comput. 45(9), 887900 (2007).CrossRef | Google Scholar | PubMed
22.NefT. and RienerR., “Shoulder Actuation Mechanisms for Arm Rehabilitation Exoskeletons,” 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (2008) pp. 862868.Google Scholar
23.OttenA.VoortC.StienenA.AartsR.van AsseldonkE. and van der KooijH., “LIMPACT: A hydraulically powered self-aligning upper limb exoskeleton,” IEEE/ASME Trans. Mechatron. 20(5), 22852298 (2015).CrossRef | Google Scholar
24.PanD.GaoF.MiaoY. and CaoR., “Co-simulation research of a novel exoskeleton-human robot system on humanoid gaits with fuzzy-PID/PID algorithms,” Adv. Eng. Software 793646 (2015).CrossRef | Google Scholar
25.PerryJ. C.RosenJ. and BurnsS., “Upper-limb powered exoskeleton design,” IEEE/ASME Trans. Mechatron. 12(4), 408417 (2007).CrossRef | Google Scholar
26.Piña-MartnezE.RobertsR.Rodriguez-LealE.Flores-ArredondoJ. H. and SotoR., “A Novel Exoskeleton for Continuous Monitoring of the Upper-Limb During Gross Motor Rehabilitation,” In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR 2016), October 18–21, 2016, Segovia, Spain (IbáñezJ.González-VargasJ.AzornJ. M.AkayM. and PonsJ. L., eds.) (Springer International PublishingCham2017) pp. 11991203.CrossRef | Google Scholar
27.RahmanM. H.RahmanM. J.CristobalO. L.SaadM.KennéJ. P. and ArchambaultP. S., “Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements,” Robotica 33(1), 1939 (2014).CrossRef | Google Scholar
28.StroppaF.LoconsoleC.MarcheschiS. and FrisoliA., “A Robot-Assisted Neuro-Rehabilitation System for Post-Stroke Patients’ Motor Skill Evaluation with ALEx Exoskeleton,” In: Converging Clinical and Engineering Research on Neurorehabilitation II (IbáñezJ.González-VargasJ.AzornJ. M.AkayM. and PonsJ. L., eds.) (Springer International PublishingCham2017) pp. 501505.Google Scholar
29.SutapunA. and SangveraphunsiriV., “A 4-DOF upper limb exoskeleton for stroke rehabilitation: Kinematics mechanics and control,” Int. J. Mech. Eng. Rob. Res. 4(3), 269272 (2015).Google Scholar
30.TangZ.ZhangK.SunS.GaoZ.ZhangL. and YangZ., “An upper-limb power-assist exoskeleton using proportional myoelectric control,” Sens. (Basel, Switzerland) 14(4), 66776694 (2014).CrossRef | Google Scholar | PubMed
31.XiaoF.GaoY.WangY.ZhuY. and ZhaoJ., “Design of a wearable cable-driven upper limb exoskeleton based on epicyclic gear trains structure,” Technol. Health Care. 25(S1), 311 (2017).CrossRef | Google Scholar | PubMed
32.GopuraR. A. R. C.BandaraD. S. V.KiguchiK. and MannG. K. I., “Developments in hardware systems of active upper-limb exoskeleton robots: A review,” Rob. Auton. Syst. 75203220 (2016).CrossRef | Google Scholar
33.IslamM.SpiewakC.RahmanM. and FarehR., “A brief review on robotic exoskeletons for upper extremity rehabilitation to find the gap between research porotype and commercial type,” Adv. Robot Autom. 6(3), (2017) pp. 112.CrossRef | Google Scholar
34.JarrasséN.ProiettiT.CrocherV.RobertsonJ.SahbaniA.MorelG. and Roby-BramiA., “Robotic exoskeletons: A perspective for the rehabilitation of arm coordination in stroke patients,” Front. Hum. Neurosci. 8(947), (2014) pp. 113.Google Scholar | PubMed
35.MaciejaszP.EschweilerJ.Gerlach-HahnK.Jansen-TroyA. and LeonhardtS., “A survey on robotic devices for upper limb rehabilitation,” J. NeuroEng. Rehabil. 11(1), 3 (2014).CrossRef | Google Scholar | PubMed
36.ChenY.LiG.ZhuY.ZhaoJ. and CaiH., “Design of a 6-DOF upper limb rehabilitation exoskeleton with parallel actuated joints,” Bio-Med. Mater. Eng. 24(6), 25272535 (2014).CrossRef | Google Scholar | PubMed
37.MadaniT.DaachiB. and DjouaniK., “Modular-controller-design-based fast terminal sliding mode for articulated exoskeleton systems,” IEEE Trans. Control Syst. Technol. 25(3), 11331140 (2017).CrossRef | Google Scholar
38.RahmanM. H.SaadM.KennéJ.-P. and ArchambaultP. S., “Control of an exoskeleton robot arm with sliding mode exponential reaching law,” Int. J. Control Autom. Syst. 11(1), 92104 (2013).CrossRef | Google Scholar
39.GopuraR. A. R. C.KiguchiK. and LiY., “SUEFUL-7: A 7DOF Upper-limb Exoskeleton Robot with Muscle-model-oriented EMG-based Control,” 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (2009) pp. 11261131.Google Scholar
40.ZeiaeeA.Soltani-ZarrinR.LangariR. and TafreshiR., “Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients,” 2017 International Conference on Rehabilitation Robotics (ICORR) (2017) pp. 759764.Google Scholar
41.FellagR.BenyahiaT.DriasM.GuiatniM. and HamerlainM., “Sliding Mode Control of a 5 Dofs Upper Limb Exoskeleton Robot,” 2017 5th International Conference on Electrical Engineering – Boumerdes (ICEE-B) (2017) pp. 16.Google Scholar
42.BabaiaslM.GoldarS. N.BarhaghtalabM. H. and MeigoliV., “Sliding Mode Control of an Exoskeleton Robot for use in Upper-limb Rehabilitation,” 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM) (2015) pp. 694701.Google Scholar
43.BrahmiB.SaadM.LunaC. O.ArchambaultP. S. and RahmanM. H., “Sliding Mode Control of an Exoskeleton Robot Based on Time Delay Estimation,” 2017 International Conference on Virtual Rehabilitation (ICVR) (2017) pp. 12.Google Scholar
44.ZhuS.JinX.YaoB.ChenQ.PeiX. and PanZ., “Non-linear sliding mode control of the lower extremity exoskeleton based on human–robot cooperation,” Int. J. Adv. Rob. Syst. 13(5), 1729881416662788 (2016).Google Scholar
45.ChenH.ChenH. and YangF., “Fractional-order Sliding-mode Stabilization of Nonholonomic Mobile Robots Based on Dynamic Feedback Linearization,” 2016 35th Chinese Control Conference (CCC) (2016) pp. 58745878.Google Scholar
46.ChengZ.MaZ.SunG. and DongH., “Fractional Order Sliding Mode Control for Attitude and Altitude Stabilization of a Quadrotor UAV,” 2017 Chinese Automation Congress (CAC) (2017) pp. 26512656.Google Scholar
47.TianyiZ.XuemeiR. and YaoZ., “A Fractional Order Sliding Mode Controller Design for Spacecraft Attitude Control System,” 2015 34th Chinese Control Conference (CCC) (2015) pp. 33793382.Google Scholar
48.TianJ.ChenN.YangJ. and WangL., “Fractional Order Sliding Model Control of Active Four-wheel Steering Vehicles,” ICFDA’14 International Conference on Fractional Differentiation and Its Applications 2014 (2014) pp. 15.Google Scholar
49.BouroubaB. and LadaciS., “Stabilization of Class of Fractional-order Chaotic System Via New Sliding Mode Control,” 2017 6th International Conference on Systems and Control (ICSC) (2017) pp. 470475.Google Scholar
50.KangJ.ZhuZ. H.WangW.LiA. and WangC., “Fractional order sliding mode control for tethered satellite deployment with disturbances,” Adv. Space Res. 59(1), 263273 (2017).CrossRef | Google Scholar
51.IslamM. R.Assad-Uz-ZamanM. and RahmanM. H., “Design and control of an ergonomic robotic shoulder for wearable exoskeleton robot for rehabilitation,” Int. J. Dyn. Control (2019) pp. 114.Google Scholar
52.CraboluM.PaniD.RaffoL.ContiM.CrivelliP. and CereattiA., “In vivo estimation of the shoulder joint center of rotation using magneto-inertial sensors: MRI-based accuracy and repeatability assessment,” Biomed. Eng. Online 16(1), 3434 (2017).CrossRef | Google Scholar | PubMed
53.HalderA. M.ItoiE. and AnK.-N., “Anatomy and biomechanics of the shoulder,” Orthop. Clinic. 31(2), 159176 (2000).Google Scholar
54.Soltani-ZarrinR.ZeiaeeA.LangariR. and TafreshiR., “A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms,” IEEE International Symposium on Wearable & Rehabilitation Robotics (WeRob2017) (Houston, Texas, USA, 2017) pp. 12.CrossRef | Google Scholar
55.CraigJ. J.Introduction to Robotics: Mechanics and Control (PearsonUpper saddle river, New Jersy2017) p. 448.Google Scholar
56.DenavitJ. and HartenbergR. S., “A kinematic notation for lower-pair mechanisms based on matrices,” Trans. of the ASME. J. Appl. Mech. 22215221 (1955).Google Scholar
57.RahmaniM. and RahmanM. H., “Novel robust control of a 7-DOF exoskeleton robot,” PLoS One 13(9), e0203440 (2018).CrossRef | Google Scholar | PubMed
58.RahmaniM.RahmanM. H. and GhommamJ., “A 7-DoF upper limb exoskeleton robot control using a new robust hybrid controller,” Int. J. Control Autom. Syst. 17(4), 986994 (2019).CrossRef | Google Scholar
59.WinterD. A., “Anthropometry,” In: Biomechanics and Motor Control of Human Movement (WinterD.A., eds.) (John Wiley & SonsNew York2009) p. 370.CrossRef | Google Scholar

via A Novel Exoskeleton with Fractional Sliding Mode Control for Upper Limb Rehabilitation | Robotica | Cambridge Core

, , , , , , , , ,

Leave a comment

[Abstract] Upper Limb Movement Modelling for Adaptive and Personalised Physical Rehabilitation in Virtual Reality – Thesis


Stroke is one of the leading causes of disability with over three-quarters of patients experiencing an upper limb impairment varying in severity. Early, intense, and frequent physical rehabilitation is important for quicker recovery of the upper limbs and the prevention of further deterioration of their upper limb impairment. Rehabilitation begins almost immediately at the hospital. Once released from the hospital it is intended that patients continue their rehabilitation program at home supported by a community stroke team. However, there are two main barriers to rehabilitation continuing effectively at this stage. The first is limited contact with a physiotherapist or occupational therapist to guide and support an intensive rehabilitation programme. The second is that conventional rehabilitation is tough to maintain immediately after stroke due to fatigue, lack of concentration, depression and other effects. Stroke patients can find exercises monotonous and tiring, and a lack of motivation can result in patients failing to engage fully with their treatment. Lack of participation in prescribed rehabilitation exercises may affect recovery or cause deterioration of mobility.

This thesis examines the hypothesis that upper limb stroke rehabilitation can be made more accessible and enjoyable through the use of modern commercial virtual reality (VR) hardware, with personalised models of user hand motion adapted to user capability over time, and VR games with tasks that utilise natural hand gestures as input controls to execute personalised physical rehabilitation exercises. To support the investigation of this hypothesis a novel adaptive, gamebased, virtual reality (VR) rehabilitation system has been designed and developed for self-managed rehabilitation. Hands are tracked using a Leap Motion Controller, with hand movements and gestures used as in input controller for VR tasks. A user-centred design methodology was adopted, and the final version of the system was evolved through several versions and iterative testing and feedback through trials with able-bodied testers, stroke survivor volunteers, and practising clinicians.

A key finding of the research was that an adapted form of Fitts’s law, that models difficulty of reaching and touching objects in 3D interaction spaces, could be used to profile movement capability for able-bodied people and stroke patients vii in upper arm VR stroke rehabilitation. It was also found that even when Fitts’s law was less effective, that the statistics of the regression quality were still informative in profiling users. Fitts law regression statistics along with information on task performance (such as percentage of hits) could be used to adapt task difficulty or advising rest. Further, it was found that multiple regression could provide better movement capability profiles with a modified form of Fitts law to account for varying degrees of difficulty due to the angles of motion in 3D space. In addition, a novel approach was developed which profiled sectors of the 3D VR interaction space separately, rather than treat movement through the whole space as being equally difficult. This approach accounts for some stroke patients having more difficulty moving in some directions than others, e.g. up and left. Results demonstrate that this has potential but may need to be investigated further with stroke patients and with larger numbers of people.

The VR system that utilised the movement capability model was evolved over time with a user-centred design methodology, with input from able-bodied people, stroke patients, and clinicians. A final longitudinal study investigated the suitability of three bespoke games, the usability of the system over a longer time, and the effectiveness of the movement profiler and adaptive system. Throughout this experiment, the system provided informative user movement profile variations that could identify unique movement behaviour traits in individuals. Results showed that user performance varied over time and the adaptive system proved effective in changing the difficulty of the tasks for individuals over multiple sessions. The VR rehabilitation games incorporated enhanced gameplay and feedback, and users expressed enjoyment with the interactive experience. Throughout all of the experiments, users enjoyed wearing a VR headset, preferring it over a standard PC monitor. Most users subjectively felt that they were more effective in completing tasks within VR, and results from experiments provided empirical evidence to support this view. Results within this thesis support the proposal that an appropriately designed, adaptive gamebased VR system can provide an accessible, personalised and enjoyable rehabilitation system that can motivate more regular rehabilitation participation and promote improved motor function.

via Upper Limb Movement Modelling for Adaptive and Personalised Physical Rehabilitation in Virtual Reality — Ulster University

, , , , , , , , , ,

Leave a comment

[Abstract] Pushing the limits of recovery in chronic stroke survivors: User perceptions of the Queen Square Upper Limb Neurorehabilitation Programme – Full Text PDF


Introduction: The Queen Square Upper Limb (QSUL) Neurorehabilitation Programme is a clinical service within the National Health Service in the United Kingdom that provides 90 hours of therapy over three weeks to stroke survivors with persistent upper limb impairment. This study aimed to explore the perceptions of participants of this programme, including clinicians, stroke survivors and carers.

Design: Descriptive qualitative.

Setting: Clinical outpatient neurorehabilitation service.

Participants: Clinicians (physiotherapists, occupational therapists, rehabilitation assistants) involved in the delivery of the QSUL Programme, as well as stroke survivors and carers who had participated in the programme were purposively sampled. Each focus group followed a series of semi-structured, open questions that were tailored to the clinical or stroke group. One independent researcher facilitated all focus groups, which were audio-recorded, transcribed verbatim and analysed by four researchers using a thematic approach to identify main themes.

Results: Four focus groups were completed: three including stroke survivors (n = 16) and carers (n = 2), and one including clinicians (n = 11). The main stroke survivor themes related to psychosocial aspects of the programme (″ you feel valued as an individual ″), as well as the behavioural training provided (″ gruelling, yet rewarding& [Prime]). The main clinician themes also included psychosocial aspects of the programme (″ patient driven ethos − no barriers, no rules ″), and knowledge, skills and resources of clinicians (″ it is more than intensity, it is complex ″).

Conclusions: As an intervention, the QSUL Programme is both comprehensive and complex. The impact of participation in the programme spans psychosocial and behavioural domains from the perspectives of both the stroke survivor and clinician.

Download Full Text PDF

via Pushing the limits of recovery in chronic stroke survivors: User perceptions of the Queen Square Upper Limb Neurorehabilitation Programme. | medRxiv

, , , , , , , , ,

Leave a comment

[Abstract] Optimized Sleep After Brain Injury (OSABI): A Pilot Study of a Sleep Hygiene Intervention for Individuals With Moderate to Severe Traumatic Brain Injury

Background. Disrupted sleep is common after traumatic brain injury (TBI) particularly in the inpatient rehabilitation setting where it may affect participation in therapy and outcomes. Treatment of sleep disruption in this setting is varied and largely unexamined.

Objective. To study the feasibility of instituting a sleep hygiene intervention on a rehabilitation unit.

Methods. Twenty-two individuals admitted to a brain injury unit were enrolled and allocated, using minimization, to either a sleep hygiene protocol (SHP) or standard of care (SOC). All participants wore actigraphs, underwent serial cognitive testing, and had light monitors placed in their hospital rooms for 4 weeks. Additionally, participants in the SHP received 30 minutes of blue-light therapy each morning, had restricted caffeine intake after noon, and were limited to 30-minute naps during the day. SHP participants had their lights out time set according to preinjury sleep time preference. Both groups were treated with the same restricted formulary of centrally acting medications.

Results. Of 258 patients screened, 27 met all study inclusion criteria of whom 22 were enrolled. Nine participants in each group who had at least 21 days of treatment were retained for analysis. The protocol was rated favorably by participants, families, and staff. Actigraph sleep metrics improved in both groups during the 4-week intervention; however, only in the SHP was the change significant.

Conclusions. Sleep hygiene is a feasible, nonpharmacologic intervention to treat disrupted sleep in a TBI inpatient rehabilitation setting. A larger study is warranted to examine treatment efficacy.

via Optimized Sleep After Brain Injury (OSABI): A Pilot Study of a Sleep Hygiene Intervention for Individuals With Moderate to Severe Traumatic Brain Injury – Michael J. Makley, Don Gerber, Jody K. Newman, Angie Philippus, Kimberley R. Monden, Jennifer Biggs, Eric Spier, Patrick Tarwater, Alan Weintraub,

, , , , , , ,

Leave a comment

[VIDEO] Addison: the Virtual Caregiver on Vimeo

The Virtual Caregiver is a next generation Virtual Assistant, bringing chronic care, rehabilitation, mental health support, caregiver support, and support for daily living unlike anything you’ve ever seen. She’s Connected Health, Digital Health, IoT, AI, AR, Natural Language, and amazing UX and UI interfaces in a breakthrough user configuration. EMR integrated, health peripherals, in-home automated exams, gait and balance, fall risk assessment, and more. Addison Care is the future, today.

via Addison: the Virtual Caregiver on Vimeo

, , , , , , , ,

Leave a comment

[ARTICLE] Upper Extremity Function Assessment Using a Glove Orthosis and Virtual Reality System – Full Text


Hand motor control deficits following stroke can diminish the ability of patients to participate in daily activities. This study investigated the criterion validity of upper extremity (UE) performance measures automatically derived from sensor data during manual practice of simulated instrumental activities of daily living (IADLs) within a virtual environment. A commercial glove orthosis was specially instrumented with motion tracking sensors to enable patients to interact, through functional UE movements, with a computer-generated virtual world using the SaeboVR software system. Fifteen stroke patients completed four virtual IADL practice sessions, as well as a battery of gold-standard assessments of UE motor and hand function. Statistical analysis using the nonparametric Spearman rank correlation reveals high and significant correlation between virtual world-derived measures and the gold-standard assessments. The results provide evidence that performance measures generated during manual interactions with a virtual environment can provide a valid indicator of UE motor status.


Virtual world-based games, when combined with human motion sensing, can enable a neurorehabilitation patient to engage in realistic occupations that involve repetitive practice of functional tasks (). An important component of such a system is the ability to automatically track patient movements and use those data to produce indices related to movement quality (). Before these technology-derived measures can be considered relevant to clinical outcomes, criterion validity must be established. If validated, measures of virtual task performance may reasonably be interpreted as reflective of real-world functional status.

The objective of the study described in this article was to investigate the criterion validity of upper extremity (UE) performance measures automatically derived from sensor data collected during practice of simulated instrumental activities of daily living (IADLs) in a virtual environment. A commercially available SaeboGlove orthosis () was specially instrumented to enable tracking of finger and thumb movements. This instrumented glove was employed with an enhanced version of the Kinect sensor-based SaeboVR software system () to enable employment of the hand, elbow, and shoulder in functional interactions with a virtual world. Performance measures were automatically generated during patient use through a combination of arm tracking data from the Kinect and the glove’s finger and thumb sensors. The primary investigational objective was to determine whether performance indices produced by this system for practice of virtual IADLs are valid indicators of a stroke patient’s UE motor status.

Previous investigations into combining hand tracking with video games to animate UE therapy have produced evidence for the efficacy of such interventions. A recent study compared a 15-session hand therapy intervention using a smart glove system and video games with a usual care regimen (). Stroke patients using the smart glove system realized greater gains in Wolf Motor Function Test (WMFT) score compared with dosage-balanced conventional therapy. Another study investigating a similar glove-based device found significantly greater improvements in Fugl-Meyer and Box and Blocks test results for stroke patients who performed 15 sessions that included the technology-aided therapy compared with subjects receiving traditional therapy only (). An instrumented glove has also been used to support video game therapy that incorporates gripping-like movements and thumb-finger opposition ().

Past research into the use of human motion tracking (sometimes referred to as motion capture) technologies for assessment of UE function has produced encouraging results. One group of researchers compared naturalistic point-to-point reaching movements with standardized reaching movements embedded in a virtual reality system, and established concurrent validity between the two (). An investigation involving a device that incorporates handgrip strength and pinch force measurement into virtual reality exercises provided support for system use as an objective evaluation of hand function, and for the potential of replacing conventional goniometry and dynamometry (). In another study, researchers employed a Kinect sensor in a software system that attempts to emulate a subset of the Fugl-Meyer Upper Extremity (FMUE) assessment (). Pearson correlation analysis between the Kinect-derived scores and traditionally administered FMUE test results for 41 hemiparetic stroke patients revealed a high correlation. Previous research involving the SaeboVR system established a moderate and statistically significant correlation between virtual IADL performance scores and the WMFT (). Due to limitations of the Kinect optical tracking system, this previous work involving the SaeboVR system did not include tracking of grasp-release manual interactions with virtual objects (). The present research addresses this limitation by fusing data from the Kinect sensor with data from finger- and wrist-mounted sensors on the SaeboGlove orthosis to reconstruct the kinematic pose of the patient’s UE.

The use of an assistive glove orthosis in the present work fills an important clinical need. Inability to bring the hand and wrist into a neutral position due to weakness and/or lack of finger extension can prevent participation in occupation-oriented functional practice (). A common technique to enable stroke patients to achieve a functional hand position (and thus participate in rehabilitation) is a dynamic splint that supports finger and/or wrist extension. When larger forces are necessary (e.g., to overcome abnormal muscle tone), an outrigger-type splint may be employed. For patients with no more than mild hypertonicity, a lower-profile device such as the SaeboGlove orthosis () can be used. Employment of an assistive glove orthosis in the context of virtual IADLs practice thus addresses some of the leading causes of hand motor control deficits following stroke and their adverse impact on ability to participate in daily activities ().



Candidates were recruited from a population of stroke patients receiving in-patient rehabilitation care, outpatient rehabilitation, or who had been previously discharged from rehabilitative care at the UVA Encompass Health Rehabilitation Hospital (Charlottesville, VA, USA). Table 1 includes the study characteristics. Of 17 patients enrolled in the study, 15 completed the protocol. One subject dropped out due to unrelated illness. A second subject was disenrolled due to an inability to adequately express an understanding of consent during re-verification at the beginning of the first post-consent study session.

Table 1.

Patient Characteristics (n = 17).

Age, years, median (range) 67 (25-83)
Time since stroke onset in months, median (range) 12 (1-72)
Sex, M/F, n (%) 10 (59)/7 (41)
Race category, Black/White, n (%) 3 (18)/14 (82)
Ethnic category, Hispanic/non-Hispanic, n (%) 0 (0)/17 (100)
Side of hemiplegia, L/R, n (%) 10 (59)/7 (41)
Affected side dominance, dominant/nondominant, n (%) 9 (53)/8 (47)

All study activities were conducted under the auspices of the University of Virginia Institutional Review Board for Health Sciences Research (IRB-HSR). All study sessions took place in a private room within the UVA Encompass Health outpatient clinic between October 20, 2017, and February 9, 2018. Licensed Occupational Therapists trained in study procedures and registered with the IRB-HSR were responsible for all patient contact, recruitment, consent, and protocol administration.

Verification of inclusion/exclusion criteria was through a three-step process including an initial medical record review prior to recruitment, verbal confirmation prior to administration of consent, and an evaluation screen conducted by a study therapist following consent. Inclusion criteria included history of stroke with hemiplegia, ongoing stroke-related hand impairment, sufficient active finger flexion at the metacarpal phalangeal joint in at least one finger to be detected by visual observation by a study therapist, antigravity strength at the elbow to at least 45° of active flexion, antigravity shoulder strength to at least 30° each in active flexion and abduction/adduction, and 15° in active shoulder rotation from an upright seated position. Participants had visual acuity with corrective lenses of 20/50 or better and were able to understand and follow verbal directions. The study excluded patients with visual field deficit in either eye that would impair ability to view the computer monitor and/or with hemispatial neglect that would impair an individual’s ability to process and perceive visual stimuli. The study also excluded individuals with motor limb apraxia, significant muscle spasticity, or contractures of the muscles, joints, tendons, ligaments, or skin that would restrict normal UE movement.


A commercial SaeboGlove orthosis was fitted with wrist and finger motion sensors to permit tracking of finger joint angles during grasp-release interactions with a virtual environment. The instrumented glove orthosis is shown in Figure 1. The sensors were attached to the existing tensioner band hooks on the dorsal side of each glove finger. An electronics enclosure mounted to the palmar side of the SaeboGlove’s plastic wrist splint processes the sensor data and transmits information to a personal computer (PC) that hosts the modified SaeboVR software. Data from both the SaeboGlove-integrated sensors and from a Kinect sensor were used by a custom motion capture algorithm, which employs a human UE kinematics model to produce real-time estimates of arm, wrist, and finger joint angles.

An external file that holds a picture, illustration, etc.Object name is 10.1177_1539449219829862-fig1.jpg

Figure 1.
SaeboGlove orthosis with sensors to track grasp interactions.



Continue —->  Upper Extremity Function Assessment Using a Glove Orthosis and Virtual Reality System

, , , , , , , , , ,

Leave a comment

[ARTICLE] Relearning functional and symmetric walking after stroke using a wearable device: a feasibility study – Full Text



Gait impairment is a common consequence of stroke and typically involves a hemiparetic or asymmetric walking pattern. Asymmetric gait patterns are correlated with decreased gait velocity and efficiency as well as increased susceptibility to serious falls and injuries.

Research Question

This paper presents an innovative device worn on a foot for gait rehabilitation post stroke. The device generates a backward motion to the foot, which is designed to exaggerate the existing step length asymmetry while walking over ground. We hypothesize this motion will decrease gait asymmetry and improve functional walking in individuals with chronic stroke.


Six participants with chronic stroke, more than one year post stroke, received four weeks of gait training with three sessions per week. Each session included 30 min of walking over ground using the wearable device. Gait symmetry and functional walking were assessed before and after training.


All participants improved step length symmetry, and four participants improved double limb support symmetry. All participants improved on all three functional outcomes (gait velocity, Timed Up and Go Test, and 6-Minute Walk Test), and five participants improved beyond the minimal detectable change or meaningful change in at least one functional outcome.


The results indicate that the presented device may help improve stroke patients’ walking ability and warrant further study. A gait training approach using this new device may enable and expand long-term continuous gait rehabilitation outside the clinic following stroke.


Each year approximately 800,000 Americans experience a new or recurrent stroke, and an estimated six million are living with gait impairments from a stroke [1]. One such disability is a ‘hemiparetic’ gait [2], which can be characterized by asymmetries in gait measures such as step length and support times [34]. Hemiparetic gait is correlated with decreased gait velocity [56], reduced walking efficiency [7], increased joint and bodily degradation [8], and increased susceptibility to injuries and falls [910].

While patients and health providers desire effective gait therapy, few effective long-term remedies have been identified. Treatments of gait commonly rely on traditional rehabilitation approaches, such as the Bobath method [1112] and lower limb strength training [1314], to re-train walking patterns. Unfortunately, results are inconsistent across patient populations with these treatment options, and there are not set devices facilitating these treatments. Some other gait correction methods currently being studied include Constraint Induced Movement Therapy [1516], body-weight support [17], robotic [18], functional electrical stimulation [19], transcranial magnetic stimulation [20], and full-body gait exoskeletons [21].

In this paper, we present a novel device (shown in Fig. 1) designed to help individuals post stroke re-learn how to walk with little to no therapeutic infrastructure needed. Unlike many of the existing gait rehabilitation devices, this device is passive, portable, wearable, and does not require any external energy. It functions by moving the nonparetic foot backward while the individual walks over ground [22]. The backward motion of the shoe is generated passively by redirecting the wearer’s downward force during stance phase [23]. Since the motion is generated by the wearer’s force, the person is in control, which allows easier adaptation to the motion, but this also causes the speed to vary slightly from person to person. The generated motion is demonstrated in Fig. 2. A height and weight matched shoe is attached to the paretic foot, but does not generate any motion.


Photo of the rehabilitative shoe that is worn on the nonparetic foot


Continue —-> Relearning functional and symmetric walking after stroke using a wearable device: a feasibility study | Journal of NeuroEngineering and Rehabilitation | Full Text

, , , , , ,

Leave a comment

[ARTICLE] Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Full Text PDF

Digital biofeedback systems (DBSs) are used in physical rehabilitation to improve outcomes by engaging and educating patients and have the potential to support patients while doing targeted exercises during home rehabilitation. The components of feedback (mode, content, frequency and timing) can influence motor learning and engagement in various ways. The feedback design used in DBSs for targeted exercise home rehabilitation, as well as the evidence underpinning the feedback and how it is evaluated, is not clearly known. To explore these concepts, we conducted a scoping review where an electronic search of PUBMED, PEDro and ACM digital libraries was conducted from January 2000 to July 2019. The main inclusion criteria included DBSs for targeted exercises, in a home rehabilitation setting, which have been tested on a clinical population. Nineteen papers were reviewed, detailing thirteen different DBSs. Feedback was mainly visual, concurrent and descriptive, frequently providing knowledge of results. Three systems provided clear rationale for the use of feedback. Four studies conducted specific evaluations of the feedback, and seven studies evaluated feedback in a less detailed or indirect manner. Future studies should describe in detail the feedback design in DBSs and consider a robust evaluation of the feedback element of the intervention to determine its efficacy.

Download Full Text PDF

via Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Sensors – X-MOL


, , , , , , , ,

Leave a comment

[Abstract] Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients


  • Twenty-one patients successfully recovered active wrist extension.
  • Motor imagery based BCI control of wrist CPM training was applied.
  • Typical spatial and spectrum patterns of ERD/ERS formed after training.


Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0 ± 15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of the affected wrists was (24.05 ± 14.46)˚. The increase of modified Barthel Index was 3.10 ± 4.02 points. But no statistical difference was detected between the baseline and final evaluations (P > 0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.


Graphical abstract

via Motor Imagery Based Brain-Computer Interface Control of Continuous Passive Motion for Wrist Extension Recovery in Chronic Stroke Patients – ScienceDirect


, , , , , , , , , , , , , , ,

Leave a comment

[Abstract] A Review on Surface Electromyography-Controlled Hand Robotic Devices Used for Rehabilitation and Assistance in Activities of Daily Living



Spinal cord injuries, traumas, natural aging, and strokes are the main causes of arm impairment or even a chronic disability for an increasing part of the population. Therefore, robotic devices can be essential tools to help individuals afflicted with hand deficit with the activities of daily living in addition to the possibility of restoring hand functions by rehabilitation. Because the surface electromyography (sEMG) control paradigm has recently emerged as an interesting intention control method in devices applied to rehabilitation, the concentration in this study has been devoted to sEMG-controlled hand robotic devices, including gloves and exoskeletons that are used for rehabilitation and for assistance in daily activities.

Materials and Methods

A brief description is given to the previous reviews and studies that have surveyed the robotic devices used for rehabilitation; a comparison is conducted among these studies with respect to the targeted part of the body and the device’s control method. Important issues about controlling by sEMG signal are accentuated, and a review of sEMG-controlled hand robotic devices is presented with an abbreviated description for each endeavor. Some criteria related to sEMG control are specifically emphasized, for instance, the muscles used for control, the number of sEMG channels, and the type of sEMG sensor used.


It is noted that most of the sEMG-based controls for the devices included in this study have used the nonpattern recognition scheme due to the weak sEMG signals and abnormal pattern of muscle activation for stroke patients. In addition to sEMG-based control, additional control paradigms have been used in many of the listed robotic devices to increase the efficacy of the system; this cooperation is required because of the difficulty in dealing with the sEMG signals of stroke patients. Most of the listed studies have conducted the experiments on a healthy subject to evaluate the efficacy of the systems, whereas the studies that have recruited stroke patients for system assessment were predominately using additional control schemes.


This article highlights the important issues about the sEMG control method and accentuates the weaknesses associated with this type of control to assist researchers in overcoming problems that impede sEMG-controlled robotic devices to be feasible and practical tools for people afflicted with hand impairment.

via A Review on Surface Electromyography-Controlled Hand Robotic… : JPO: Journal of Prosthetics and Orthotics

, , , , , , ,

Leave a comment

%d bloggers like this: