Movement variability in stroke patients and controls performing two upper limb functional tasks: a new assessment methodology

Background: In the evaluation of upper limb impairment post stroke there remains a gap between detailed kinematic analyses with expensive motion capturing systems and common clinical assessment tests. In particular, although many clinical tests evaluate the performance of functional tasks, metrics to characterise upper limb kinematics are generally not applicable to such tasks and very limited in scope. This paper reports on a novel, user-friendly methodology that allows for the assessment of both signal magnitude and timing variability in upper limb movement trajectories during functional task performance. In order to demonstrate the technique, we report on a study in which the variability in timing and signal magnitude of data collected during the performance of two functional tasks is compared between a group of subjects with stroke and a group of individually matched control subjects. Methods: We employ dynamic time warping for curve registration to quantify two aspects of movement variability: 1) variability of the timing of the accelerometer signals' characteristics and 2) variability of the signals' magnitude. Six stroke patients and six matched controls performed several trials of a unilateral ('drinking') and a bilateral ('moving a plate') functional task on two different days, approximately 1 month apart. Group differences for the two variability metrics were investigated on both days. Results: For 'drinking from a glass' significant group differences were obtained on both days for the timing variability of the acceleration signals' characteristics (p = 0.002 and p = 0.008 for test and retest, respectively); all stroke patients showed increased signal timing variability as compared to their corresponding control subject. 'Moving a plate' provided less distinct group differences. Conclusion: This initial application establishes that movement variability metrics, as determined by our methodology, appear different in stroke patients as compared to matched controls during unilateral task performance ('drinking'). Use of a user-friendly, inexpensive accelerometer makes this methodology feasible for routine clinical evaluations. We are encouraged to perform larger studies to further investigate the metrics' usefulness when quantifying levels of impairment

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Developing Predictive Models for Upper Extremity Post–Stroke Motion Quality Estimation Using Decision Trees and Bagging Forest

Stroke is one of the leading causes of long–term disability. Approximately twothirds of stroke survivors require long-term rehabilitation, which suggests the importance of understanding the post-stroke recovery process during his activities of daily living. This problem is formulated as quantifying and estimating the poststroke movement quality in real world settings. To address this need, we have developed an approach that quantifies physical activities and can evaluate the performance quality. Wearable accelerometer and gyroscope are used to measure the upper extremity motions and to develop a mathematical framework to objectively relates sensors’ data to clinical performance indices. In this article we employ two machine learning classification methods, Bootstrap Aggregating (Bagging) Forest and Decision Tree (DT), to relate the post-stroke kinematic data to quality of the corresponding motion. We then compare the accuracy of the resulted two prediction models using cross-validation approaches. Our findings indicate that Bagging forest approach is superior to the computationally simpler DTs for unstable data sets including those derived from stroke survivors in this project

Kinematic analysis of reaching movements of the upper limb after total or reverse shoulder arthroplasty

Studies have analyzed three-dimensional complex motion of the shoulder in healthy subjects or patients undergoing total shoulder arthroplasty (TSA) or reverse shoulder arthroplasty (RSA). No study to date has assessed the reaching movements in patients with TSA or RSA. Twelve patients with TSA (Group A) and 12 with RSA (Group B) underwent kinematic analysis of reaching movements directed at four targets. The results were compared to those of 12 healthy subjects (Group C). The assessed parameters were hand-to-target distance, target-approaching velocity, humeral-elevation angular velocity, normalized jerk (indicating motion fluidity), elbow extension and humeral elevation angles. Mean Constant score increased by 38 points in Group A and 47 in Group B after surgery. In three of the tasks, there were no significant differences between healthy subjects and patients in the study groups. Mean target-approaching velocity and humeral-elevation angular velocity were significantly greater in the control group than in study groups and, overall, greater in Group A than Group B. Movement fluidity was significantly greater in the controls, with patients in Group B showing greater fluidity than those in Group A. Reaching movements in the study groups were comparable, in three of the tasks, to those in the control group. However, the latter performed significantly better with regard to target-approaching velocity, humeral-elevation angular velocity and movement fluidity, which are the most representative characteristics of reaching motion. These differences, that may be related to deterioration of shoulder proprioception after prosthetic implant, might possibly be decreased with appropriate rehabilitation

Kinematic synergies of hand grasps ::a comprehensive study on a large publicly available dataset

Background: Hand grasp patterns require complex coordination. The reduction of the kinematic dimensionality is a key process to study the patterns underlying hand usage and grasping. It allows to define metrics for motor assessment and rehabilitation, to develop assistive devices and prosthesis control methods. Several studies were presented in this field but most of them targeted a limited number of subjects, they focused on postures rather than entire grasping movements and they did not perform separate analysis for the tasks and subjects, which can limit the impact on rehabilitation and assistive applications. This paper provides a comprehensive mapping of synergies from hand grasps targeting activities of daily living. It clarifies several current limits of the field and fosters the development of applications in rehabilitation and assistive robotics. Methods: In this work, hand kinematic data of 77 subjects, performing up to 20 hand grasps, were acquired with a data glove (a 22-sensor CyberGlove II data glove) and analyzed. Principal Component Analysis (PCA) and hierarchical cluster analysis were used to extract and group kinematic synergies that summarize the coordination patterns available for hand grasps. Results: Twelve synergies were found to account for > 80% of the overall variation. The first three synergies accounted for more than 50% of the total amount of variance and consisted of: the flexion and adduction of the Metacarpophalangeal joint (MCP) of fingers 3 to 5 (synergy #1), palmar arching and flexion of the wrist (synergy #2) and opposition of the thumb (synergy #3). Further synergies refine movements and have higher variability among subjects. Conclusion: Kinematic synergies are extracted from a large number of subjects (77) and grasps related to activities of daily living (20). The number of motor modules required to perform the motor tasks is higher than what previously described. Twelve synergies are responsible for most of the variation in hand grasping. The first three are used as primary synergies, while the remaining ones target finer movements (e.g. independence of thumb and index finger). The results generalize the description of hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics

A system to provide guidance to stroke patients during independent physiotherapy

Stroke is a serious disease that leaves many sufferers physically disabled. Treatment resources are limited, meaning stroke patients, are in many cases, discharged prior to reaching their full potential of physical recovery. The hypothesis of this research is that a system that enables regular guided and monitored therapeutic exercises in the home can provide a means for stroke patients to achieve a higher level of physical rehabilitation. This research is based on the design, build and testing of an experimental prototype system to allow this, with the aim of investigating the feasibility and potential value for such systems. Any system to assist rehabilitation in the home must clearly be low cost, safe and easy to use. The prototype system therefore aimed to achieve these features as well as focusing on the upper limb. Literature is reviewed in the fields of stroke, human anatomy and mechanisms, motor performance, feedback during motor learning, and existing systems and technology. Interviews are also conducted with stroke physiotherapists to gain input and feedback on concepts that were generated. Although systems exist with similar aims to those mentioned in the hypothesis, there are some areas where investigation is lacking. The prototype system measures movement using a novel combination of gyro sensors and flex sensors. The prototype system is designed with a focus on the method of interaction with patients and the provision of guidance and feedback that simulates that provided by a physiotherapist. The prototype system also provides a unique combination of quantitative information to patients of their personal improvements and graphical feedback of their movements and target movements. Finally, a novel categorisation of movement synergism (a form of movement coordination) is established and a novel method for detecting movement synergism is developed and tested. Performance of the prototype hardware is tested, and it is concluded that identified requirements have been met, although variability of recorded data is high. Tests also indicate that the prototype system is capable of detecting movement synergism. Finally, a controlled test involving healthy participants is performed to investigate the efficacy of the prototype as a whole. It was found that use of the prototype system resulted in a statistically significant improvement in conformance to target movements (ρ < 0.05). Findings are discussed in detail and the hypothesis is concluded as being supported overall. Recommendations for future research are made

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation