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

Objective evaluation of the quality of movement in daily life after stroke

Stroke survivors are commonly left with disabilities that impair activities of daily living. The main objective of their rehabilitation program is to maximize the functional performance at home. However, the actual performance of patients in their home environment is unknown. Therefore, objective evaluation of daily life activities of stroke survivors in their physical interaction with the environment is essential for optimal guidance of rehabilitation therapy. Monitoring daily life movements could be very challenging, as it may result in large amounts of data, without any context. Therefore, suitable metrics are necessary to quantify relevant aspects of movement performance during daily life. The objective of this study is to develop data processing methods, which can be used to process movement data into relevant metrics for the evaluation of intra-patient differences in quality of movements in a daily life setting. Based on an iterative requirement process, functional and technical requirements were formulated. These were prioritized resulting in a coherent set of metrics. An activity monitor was developed to give context to captured movement data at home. Finally, the metrics will be demonstrated in two stroke participants during and after their rehabilitation phases. By using the final set of metrics, quality of movement can be evaluated in a daily life setting. As example to demonstrate potential of presented methods, data of two stroke patients were successfully analyzed. Differences between in-clinic measurements and measurements during daily life are observed by applying the presented metrics and visualization methods. Heel height profiles show intra-patient differences in height, distance, stride profile, and variability between strides during a 10-m walk test in the clinic and walking at home. Differences in distance and stride profile between both feet were larger at home, than in clinic. For the upper extremities, the participant was able to reach further away from the pelvis and cover a larger area. Presented methods can be used for the objective evaluation of intra-patient differences in movement quality between in-clinic and daily life measurements. Any observed progression or deterioration of movement quality could be used to decide on continuing, stopping, or adjusting rehabilitation programs

Modeling and Simulation of Lower Limb Spasticity in Motor-Impaired Individuals

Spasticity is a symptom that impairs the ability to freely move and control one’s limbs through increased tone and involuntary activations in the muscles. It can cause pain and discomfort and interfere with daily life and activities such as walking. Spasticity is a result of upper motor neuron lesions and is seen commonly in survivors of stroke and brain trauma, and individuals with cerebral palsy, multiple sclerosis, and spinal cord injuries. Despite its ubiquity the phenomena is not well understood. However, the most referred to definition describes spasticity as “a velocity-dependent increase in tonic stretch reflexes with exaggerated tendon jerks, resulting from hyper-excitability of the stretch reflexes.” Qualitative, subjective measures are commonly used in the clinical setting to assess spasticity, most notably the Modified Ashworth score, which has been shown to have inconsistent reliability, relying heavily on the examiner’s experience, and is inaccurate for the lower limbs. Furthermore, these subjective scores do not account for the velocity-dependence of spasticity, which is a key differentiator against other symptoms such as rigidity. Consequently, there is a need for an objective measure of spasticity that can provide a more accurate and reliable alternative or supplement to the current clinical practice, in order to improve the evaluation of treatment and rehabilitation for spasticity. To address this need, a system was developed, validated and applied for modeling the spasticity in the lower-limbs of an affected individual. An experimental setup consisted of a brace-handle system with integrated force sensors for passive actuation of each leg segment, stretching spastic muscles to assess the severity of the condition. The setup included wearable sensors sEMG and IMUs – recording muscular activity and limb segment kinematics respectively during these motions. From the data, onsets of muscular activity and subsequently the trigger points of spastic reflexes were identified, which were mapped onto the calculated joint kinematics. Based on threshold-control theory, stretch reflex threshold (SRT) models of spasticity were created for each muscle by plotting the joint velocities and positions and using regression analysis to create a dynamic threshold in the kinematic space that divided the regimes of spastic and non-spastic motion. These muscle-specific models were combined by muscle groups, leading to the creation of a novel, data-based measure that characterizes the severity of spasticity of a group of muscles. The models and measures were found to agree with the expected changes from different conditions of muscle stretch, and different levels of spasticity in the included subjects, but required more data for statistical validation. The muscle-specific models were then implemented in a spasticity controller developed for use in neuromuscular simulations, in addition to further modeling of spastic reflex characteristics. The controller was applied in a scenario simulation of the same passive movement spasticity assessments used to collect the original data, which provided additional validation of the methodology and results of the modeling. The spasticity controller was also applied in a previously developed reinforcement-learning walking agent, to see the effects of spasticity on simulated gait. Following modification and training of the new agents, the spatio-temporal parameters of gait were analyzed to determine the differences in healthy and spastic gait, which agreed with expectations and further validated the spasticity modeling. This thesis presents a system to accurately and reliably model spasticity, establishing a novel, objective measure to better characterize spasticity, validating it through demonstrations of its use that may be extended in future work to accomplish better understanding of spasticity and provide invaluable improvements to the lives of affected individuals through practical applications

Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark

A deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment

Moving On:Measuring Movement Remotely after Stroke

Most persons with stroke suffer from motor impairment, which restricts mobility on one side, and affects their independence in daily life activities. Measuring recovery is needed to develop individualized therapies. However, commonly used clinical outcomes suffer from low resolution and subjectivity. Therefore, objective biomechanical metrics should be identified to measure movement quality. However, non-portable laboratory setups are required in order to measure these metrics accurately. Alternatively, minimal wearable systems can be developed to simplify measurements performed at clinic or home to monitor recovery. Thus, the goal of the thesis was ‘To identify metrics that reflect movement quality of upper and lower extremities after stroke and develop wearable minimal systems for tracking the proposed metrics’. Section Upper Extremity First, we systematically reviewed literature ( Chapter II ) to identify metrics used to measure reaching recovery longitudinally post-stroke. Although several metrics were found, it was not clear how they differentiated recovery from compensation strategies. Future studies must address this gap in order to optimize stroke therapy. Next, we assessed a ‘valid’ measure for smoothness of upper paretic limb reaching ( Chapter III ), as this was commonly used to measure movement quality. After a systematic review and simulation analyses, we found that reaching smoothness is best measured using spectral arc length. The studies in this section offer us a better understanding of movement recovery in the upper extremity post-stroke. Section Lower Extremity Although metrics that reflect gait recovery are yet to be identified, in this section we focused on developing minimal solutions to measure gait quality. First, we showed the feasibility of 1D pressure insoles as a lightweight alternative for measuring 3D Ground Reaction Forces (GRF) ( Chapter IV ). In the following chapters, we developed a minimal system; the Portable Gait Lab (PGL) using only three Inertial Measurement Units (IMUs) (one per foot and one on the pelvis). We explored the Centroidal Moment Pivot (CMP) point ( Chapter V ) as a biomechanical constraint that can help with the reduction in sensors. Then, we showed the feasibility of the PGL to track 3D GRF ( Chapters VI-VII ) and relative foot and CoM kinematics ( Chapter VIII-IX ) during variable overground walking by healthy participants. Finally, we performed a limited validation study in persons with chronic stroke ( Chapter X ). This thesis offers knowledge and tools which can help clinicians and researchers understand movement quality and thereby develop individualized therapies post-stroke

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

International Conference on NeuroRehabilitation 2012

This volume 3, number 2 gathers a set of articles based on the most outstanding research on accessibility and disability issues that was presented in the International Conference on NeuroRehabilitation 2012 (ICNR).The articles’ research present in this number is centred on the analysis and/or rehabilitation of body impairment most due to brain injury and neurological disorders.JACCES thanks the collaboration of the ICNR members and the research authors and reviewers that have collaborated for making possible that issue