1.Rehabilitation 2030: A call for action plan: Then need to scale up rehabilitation (2017)Google Scholar
3.Union Européenne des Médicins Spécialistes (UEMS) e Académie Européenne de Médicine e Réadaptation: Livro Branco de Medicina Física e de Reabilitação na Europa. Sociedade Portuguesa de Medicina Fisica e de Reabilitação, Coimbra (2009)Google Scholar
4.Turolla, A.: An overall framework for neurorehabilitation robotics: implications for recovery. In: Rehabilitation Robotics, pp. 15–27. Elsevier (2018)Google Scholar
8.Casadio, M., Sanguineti, V., et al.: Braccio di Ferro: a new haptic workstation for neuromotor rehabilitation. Technol. Health Care (14), 123–142 (2006)Google Scholar
9.Amirabdollahian, F., Taylor, M., et al.: The Gentle/S project: a new method of delivering neuro-rehabilitation. Assistive Technology – Added Value to the Quality of Life (10), 36–41 (2001)Google Scholar
10.Kemna, S., Culmer, P., et al.: Developing a user interface for the iPAM stroke rehabilitation system. In: IEEE International Conference on Rehabilitation Robotics, Kyoto, Japan (2009)Google Scholar
11.Hogan, N., Krebs, H.I., Charnnarong, J., Srikrishna, P., Sharon, A.: Mit-manus: a workstation for manual therapy and training. I. In: Proceedings IEEE International Workshop on Robot and Human Communication, pp. 161–165. IEEE (1992)Google Scholar
12.Reharob: Reharob (2000). http://reharob.manuf.bme.hu. Accessed 06 May 2019
14.Coppelia Robotics. http://www.coppeliarobotics.com. Accessed 17 June 2019
15.Coppelia Robotics Homepage: Max. joint torques – 17260. https://www.universal-robots.com/how-tos-and-faqs/faq/ur-faq/max-joint-torques-17260/. Accessed 02 Sept 2019
16.Ribeiro, D.C., Estivalet, M.G., Loss, J.F.: Modelo para estimativa da força e torque muscular durante a abdução do ombro. revista portuguesa de ciências do desporto 8(3), 321–329 (2008)Google Scholar
Posts Tagged Activities of daily living
[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 (Adams et al., 2018). 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 (Adams et al., 2015). 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 (SaeboGlove, 2018) 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 (SaeboVR, 2018) 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 (Jung et al., 2017). 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 (Carmeli, Peleg, Bartur, Elbo, & Vatine, 2011). An instrumented glove has also been used to support video game therapy that incorporates gripping-like movements and thumb-finger opposition (Chan et al., 2014).
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 (Schaefer & Hengge, 2016). 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 (Nica, Brailescu, & Scarlet, 2013). 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 (Kim, Cho, Baek, Bang, & Paik, 2016). 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 (Adams et al., 2015). 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 (Adams et al., 2018). 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 (Lang, DeJong, & Beebe, 2009). 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 (SaeboGlove, 2018) 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 (Kamper, Fischer, Cruz, & Rymer, 2006; Ng, Tsang, Kwong, Tse, & Wong, 2011).
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.
|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.
[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.
[Abstract] Robotic-assisted therapy with bilateral practice improves task and motor performance in the upper extremities of chronic stroke patients: A randomised controlled trial.
Task-specific repetitive training, a usual care in occupational therapy practice, and robotic-aided rehabilitation with bilateral practice are used to improve upper limb motor and task performance. The difference in effects of two strategies requires exploration. This study compared the impact of robotic-assisted therapy with bilateral practice (RTBP) and usual task-specific training facilitated by therapists on task and motor performance for stroke survivors.
Forty-three community-dwelling stroke survivors (20 males; 23 females; 53.3 ± 13.1 years; post-stroke duration 14.2 ± 10.9 months) were randomised into RTBP and usual care. All participants received a 10-minute per-protocol sensorimotor stimulation session prior to interventions as part of usual care. Primary outcome was different in the amount of use (AOU) and quality of movement (QOM) on the Motor Activity Log (MAL) scale at endpoint. Secondary outcomes were AOU and QOM scores at follow-up, and pre-post and follow-up score differences on the Fugl-Meyer Assessment (FMA) and surface electromyography (sEMG). Friedman and Mann-Whitney U tests were used to calculate difference.
There were no baseline differences between groups. Both conditions demonstrated significant within-group improvements in AOU-MAL and FMA scores following treatment (P < 0.05) and improvements in FMA scores at follow-up (P < 0.05). The training-induced improvement in AOU (30.0%) following treatment was greater than the minimal detectable change (16.8%) in the RTBP group. RTBP demonstrated better outcomes in FMA wrist score (P = 0.003) and sEMG of wrist extensor (P = 0.043) following treatment and in AOU (P < 0.001), FMA total score (P = 0.006), FMA wrist score (P < 0.001) and sEMG of wrist extensor (P = 0.017) at follow-up compared to the control group. Control group boost more beneficial effects on FMA hand score (P = 0.049) following treatment.
RTBP demonstrated superior upper limb motor and task performance outcomes compared to therapists-facilitated task training when both were preceded by a 10-minute sensorimotor stimulation session.
[Abstract + References] Using a Collaborative Robot to the Upper Limb Rehabilitation – Conference paper
Rehabilitation is a relevant process for the recovery from dysfunctions and improves the realization of patient’s Activities of Daily Living (ADLs). Robotic systems are considered an important field within the development of physical rehabilitation, thus allowing the collection of several data, besides performing exercises with intensity and repeatedly. This paper addresses the use of a collaborative robot applied in the rehabilitation field to help the physiotherapy of upper limb of patients, specifically shoulder. To perform the movements with any patient the system must learn to behave to each of them. In this sense, the Reinforcement Learning (RL) algorithm makes the system robust and independent of the path of motion. To test this approach, it is proposed a simulation with a UR3 robot implemented in V-REP platform. The main control variable is the resistance force that the robot is able to do against the movement performed by the human arm.
[Abstract] BIGHand – A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise – Demo Video
Effective home rehabilitation is important for recovery of hand grip ability in post-stroke individuals. This paper presents BIGHand, a bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise. BIGHand consists of affordable sensor-integrated hardware (Vernier hand dynamometers, Arduino Uno, interface shield) used to obtain real-time grip force data, and a set of exergames designed as parts of an interactive structural rehabilitation program. This program pairs targeted difficulty progression with user-ability scaled controls to create an adaptive, challenging, and enticing rehabilitation environment. This training prepares users for the many activities of daily living (ADLs) by targeting strength, bilateral coordination, hand-eye coordination, speed, endurance, precision, and dynamic grip force adjustment. Multiple measures are taken to engage, motivate, and guide users through the at-home rehabilitation process, including “smart” post-game feedback and in-game goals.
[Abstract] Effects of Bihemispheric Transcranial Direct Current Stimulation on Upper Extremity Function in Stroke Patients: A randomized Double-Blind Sham-Controlled Study
RehaCom is a modular software used for cognitive therapy. It assists therapist in the rehabilitation of cognitive disorders that affect specific aspects of attention, concentration, memory, perception, activities of daily living and much more.
[Abstract] A systematic review of personal smart technologies used to improve outcomes in adults with acquired brain injuries
This review aimed to determine the effectiveness of personal smart technologies on outcomes in adults with acquired brain injury.
A systematic literature search was conducted on 30 May 2019. Twelve electronic databases, grey literature databases, PROSPERO, reference list and author citations were searched.
Randomised controlled trials were included if personal smart technology was used to improve independence, goal attainment/function, fatigue or quality of life in adults with acquired brain injury. Data were extracted using a bespoke form and the TIDieR checklist. Studies were graded using the PEDro scale to assess quality of reporting. Meta-analysis was conducted across four studies.
Six studies met the inclusion criteria, generating a total of 244 participants. All studies were of high quality (PEDro ⩾ 6). Interventions included personal digital assistant, smartphone app, mobile phone messaging, Neuropage and an iPad. Reporting of intervention tailoring for individual needs was inconsistent. All studies measured goal attainment/function but none measured independence or fatigue. One study (n = 42) reported a significant increase in memory-specific goal attainment (p = 0.0001) and retrospective memory function (p = 0.042) in favour of the intervention. Another study (n = 8) reported a significant increase in social participation in favour of the intervention (p = 0.01). However, our meta-analyses found no significant effect of personal smart technology on goal attainment, cognitive or psychological function.
ADLs Are Where the Repetitions Are
Brain plasticity is amazing, but rewiring the brain requires thousands of repetitions (reps). Activities of Daily Living (ADLs) are a great way to get the reps needed to retrain the brain.
Four examples show why three sets of ten each day cannot compete with ADLs.
1) Twice a day I open my hemiplegic (paralyzed) hand to grasp a tube of toothpaste so my sound hand can remove the cap. My hand opens again to hold the tube while I put the cap back on. In nine years I have opened my hand over 5000 times before brushing my teeth.
2) I have to turn 14 times to prepare cereal with a sliced banana. I have made this same breakfast for nine years so I have made over 45,000 turns. Add making a sandwich for lunch and preparing a hot meal for dinner and the number of turns I have made in the kitchen are in the hundreds of thousands.
3) Shopping is therapy for my hand. I open my hemiplegic hand to let go of the cart and reach for items with my sound hand. My hemiplegic hand opens a 2nd time when I grab the cart to move on. My hemiplegic hand opens a 3rd time so I can let go of the cart so I can maneuver to empty the cart in the check-out lane and again to load food into my car. Pick up 30 items + empty cart + load car means I open my hand 60 + 2 + 2 = 64 times. 64 x 2 visits a week x 9 years means I have opened my hemiplegic hand 59,904 times in the grocery store.
4) The distance I have walked at the grocery store is huge. I step away from the shopping cart and bend down or reach up to get items I want. The S-shaped curves I make to detour around people and other carts require more steps than walking in a straight line. According to my pedometer I walk 2,000+ steps each time I visit the grocery store. 2,000 x 2 visits a week x nine years = 1,872,000 steps!