Posts Tagged TASK ANALYSIS
[ARTICLE] Portable Motion-Analysis Device for Upper-Limb Research, Assessment, and Rehabilitation in Non-Laboratory Settings – Full Text
An integral part of clinical care for individuals with motor disorders is to assess motor function to guide and evaluate medical treatment, surgical intervention or physical therapy. One of the challenges for assessing motor function is to define sensitive and quantitative measures that can be readily obtained in clinical practice. The objective of this study was to develop a device that affords quantitative assessment of motor impairments in non-laboratory settings. The specific focus is on individuals with upper-limb movement disorders. One central goal was to ground the task in scientific research to relate clinical measures to research and capitalize on insights from fundamental research.
This paper first lays out the need for such a device particularly for children with motor disorders and post-stroke rehabilitation. We then motivate the specific motor task that was originally conceived for basic research on motor control. We then detail the design of the prototype with all hardware and software components so that it can be replicated. One design goal was to make the device low-cost, so that it can be used in many clinical environments including at home for therapeutic exercises. We conclude with first results from pilot experiments acquired both in a traditional laboratory setting and in an Epilepsy Monitoring Unit. These first data were obtained from children with dystonia. However, the device is not limited to this population and is currently further modified for the assessment of stroke patients.
A. Clinical Assessments of Motor Disorders
A motor disorder manifests as an impaired ability to execute a movement with the intended spatial and temporal pattern. This includes abnormal posturing, presence of unintended excessive movement, and normal movements occurring at unintended or inappropriate times . Patients with upper-limb impairments require special assistance to perform common motor tasks associated with self-care, such as feeding and dressing. Challenges in their movement control result in frustration, which leads to less engagement and practice, and thereby fewer opportunities to attenuate their motor disabilities and improve their movement control.
Motor disorder are observed also among children. Cerebral Palsy (CP) is a common cause of movement disorders among children, affecting 3 to 4 individuals per 1000 births in the US. The dyskinetic form of CP occurs in 15% of all cases . Due to inflexible postures, caused by muscle spasms and contractures together with involuntary jerky movements, children with dyskinetic CP are often prevented from participation in many daily activities. This also prevents them from acquiring age-appropriate motor skills during critical periods of skill development , . This is particularly aggravated when the condition affects the upper limbs.
For clinical motor assessments, the current standard tools are clinical scales. For cerebral palsy, typical tests are the gross motor function classification system (GMFCS) , the manual ability classification system (MACS) , the House Scale , the Melbourne Assessment , the Assisting Hand Assessment , the Hypertonia Assessment Tool (HAT) , the Barry-Albright Dystonia (BAD) scale , and the Shriners Hospital for Children Upper Extremity Evaluation . These outcome measures were devised to satisfy the typical criteria for effective outcome measures, including reliability, validity, specificity, and responsiveness . Although useful, these rating scales rely on subjective assessment and questionnaires that are vulnerable to inter-rater and test-retest reliability, nonlinearity, multi-dimensionality, and ceiling or floor effects . These shortcomings need to be overcome by more quantitative outcome measures to provide a better evaluation of the individual’s motor functions and abilities, and potentially utilize such measures to objectvely assess and titrate interventions.
B. Quantitative Assessment of Motor Function
Motion tracking technologies have provided quantitative means of recording movements through a variety of sensing technology that tracks and stores movement. Camera-based motion capture, such as Vicon (Vicon Motion Systems, Oxford, UK) and Optitrak (Northern Digital Inc, Ontario, CA) requires external markers or sensors placed on key anatomical landmarks to reconstruct the skeletal model of human body parts. These state-of-the-art technologies track motion to very high precision with high sampling rates and they have been used for pre- and post-treatment assessment of upper- or lower-extremity pathologies. However, such data acquisition is limited to traditional laboratory settings because the multi-camera systems are expensive and not portable.
On the other hand, there are low-cost inertial measurement units (IMUs) that directly measure acceleration, rotational change and magnetic orientation. While these sensors have the advantage that they are self-contained and wearable, drawbacks are degraded accuracy due to drift, calibration errors and noise inherent to inertial sensors and the need to frequently recharge batteries for real-time data streaming . Moreover, attaching sensors to body parts can be inconvenient or even impossible for certain clinical populations, and many children will not tolerate them.
In view of the above arguments, there is a strong need for less invasive devices that can provide quantitative measurements in tasks related to upper-extremeity motor function. Preferably, such a device should allow for portability and be low-cost to reach large populations.
C. Low-Cost Rehabilitation at Home
Rehabilitation follows standard practice and frequently requires one-on-one interaction with a therapist for extended periods of time. For these reasons, robotic devices have emerged to deliver higher-dosage and higher-intensity training for patients with movement disorders such as cerebral palsy and stroke –. However, while effective, robotic therapy is expensive and to date can only be used in clinical settings. To increase the volume in therapy, lower-cost devices that can be used at home are urgently needed.
Performance improvements with predominant home training are indeed possible. This was demonstrated by pediatric constraint-induced movement therapy (CIMT) for children with hemiparetic CP , . Further, it was shown that even children with severe dystonia can improve their performance if they use an interface or device that enables and facilitates their severely handicapped movements .
A portable low-cost device for home use that is able to provide reliable quantitative measurements would help address the above shortcomings. Measurements could also be streamed to careproviders on a secure cloud protocol, for diagnosis of interventions, analysis of therapeutic outcomes, and further follow up.
D. Theoretically-Grounded Rehabilitation
Motor tasks for home therapy should be engaging to avoid boredom and attrition and should also have functional relevance. With this goal in mind, we developed a motor task that was motivated by the daily self-feeding activity of leading a cup of coffee or a spoon filled with soup to the mouth. The core challenge of actions of this kind is that moving such an object with sloshing liquid presents complex interaction forces: any force applied to the cup also applies a force to the liquid that then acts back on the hand. When such internal dynamics is present, interaction forces become quite complex, and the human performing the task needs to predict and preempt the internal dynamics of the moving liquid. Clearly, better understanding task is like guiding a cup of coffee to one’s mouth or a spoonful of soup has high functional relevance. While many such functional tasks have been developed for rehabiltation (e.g., the box-and-block and the pegboard task), the quantitative assessment should allow for more than descriptive outcome measures such as error or success rate. Monitoring the ‘process’ continuously should provide more detailed insight into coordinative challenges. This is indeed possible in the task of guiding a cup of coffee as we explain next.
In previous research, we abstracted a relevant, yet simplified model task, inspired by guiding a cup of coffee –. To reduce the complexity and afford theoretical analyses, the “cup of coffee” was simplified to a rigid object with a rolling ball inside. The rolling ball represents the moving liquid; this is also similar to the children’s game of transporting an egg in a spoon . Fig.1A-C shows the transition from the real object to the simplified physical model. Importantly, the original task (Fig.1A) was reduced to a two-dimensional model, where the subject interacts with the object via a robotic manipulandum. The virtual model consists of a cart with a suspended pendulum, a well-known benchmark problem in control theory.
[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation
2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.
3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.
4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.
5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.
6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.
7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.
8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.
9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.
10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.
11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.
12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.
13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.
14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.
15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.
16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)
17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.
18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.
19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.
[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation
[Abstract] The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study
[Abstract + References] Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions
Muscle activation during movement is commonly disrupted due to neural injuries from stroke. A major challenge for stroke rehabilitation is to re-establish the normal ways of muscle activation through a general restoration of motor control, otherwise impairments may be compensated by the motor system through a substitution strategy of task control . In post-stroke intervention, new technologies such as neuromuscular electrical stimulation (NMES) or functional electrical stimulation (FES) offer advantages for non-invasively targeting specific groups of muscles – to restore the pattern of muscle activation. Nevertheless, their effectiveness is limited by lack of a systematic methodology to optimize the stimulation pattern, to implement the optimal strategy in clinical settings, and to design a protocol of training towards the goal of restoring motor functions. This pioneer study addresses these issues in clinical application with a non-invasive FES technology.
[Abstract] A Virtual Reality based Training and Assessment System for Hand Rehabilitation – IEEE Conference Publication
In recent years, according to American Heart Association, stroke is the leading cause of serious long-term disability in the US and about 795,000 people suffer from a stroke each year . China is also facing the same problem. The stroke is the first leading cause of death. Every year, 2.4 million people suffer from stroke . Fortunately, about 60-75 percent of those can survive. However, about 65 percent of them still remain severely handicapped because of the neurological damage caused by stroke, for example, movement disorders, hemiparesis and so on , . Those sequelae have an effect on body movement function, especially arm and hand function , . The lost of hand movement function will affect the Activities of Daily Living (ADLs), which will decrease the quality of life .[…]
[Abstract + References] Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication
[Abstract + References] Patient Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use – IEEE Conference Publication