No abstract

BackgroundStroke 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.MethodsBased 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.ResultsBy 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.DiscussionPresented 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.

BackgroundUpper-limb impairments in stroke patients are usually measured in clinical setting using standard clinical assessment. In addition, kinematic analysis using opto-electronic systems has been used in the laboratory setting to map arm recovery. Such kinematic measurements cannot capture the actual function of the upper extremity in daily life. The aim of this study is to longitudinally explore the complementarity of post-stroke upper-limb recovery measured by standard clinical assessments and daily-life recorded kinematics.MethodsThe study was designed as an observational, single-group study to evaluate rehabilitation progress in a clinical and home environment, with a full-body sensor system in stroke patients. Kinematic data were recorded with a full-body motion capture suit during clinical assessment and self-directed activities of daily living. The measurements were performed at three time points for 3 h: (1) 2 weeks before discharge of the rehabilitation clinic, (2) right after discharge, and (3) 4 weeks after discharge. The kinematic analysis of reaching movements uses the position and orientation of each body segment to derive the joint angles. Newly developed metrics for classifying activity and quality of upper extremity movement were applied.ResultsThe data of four stroke patients (three mildly impaired, one sever impaired) were included in this study. The arm motor function assessment improved during the inpatient rehabilitation, but declined in the first 4 weeks after discharge. A change in the data (kinematics and new metrics) from the daily-life recording was seen in in all patients. Despite this worsening patients increased the number of reaches they performed during daily life in their home environment.ConclusionIt is feasible to measure arm kinematics using Inertial Measurement Unit sensors during daily life in stroke patients at the different stages of rehabilitation. Our results from the daily-life recordings complemented the data from the clinical assessments and illustrate the potential to identify stroke patient characteristics, based on kinematics, reaching counts, and work area.Clinical Trial Registration, identifier NCT02118363.

Currently, the changes in functional capacity and performance of stroke patients after returning home from a rehabilitation hospital is unknown to a physician, having no objective information about the intensity and quality of a patient’s daily-life activities. Therefore, there is a need to develop and validate an unobtrusive and modular system for objectively monitoring the stroke patient’s upper and lower extremity motor function in daily-life activities and in home training. This is the main goal of the European FP7 project named “INTERACTION”. A complete full body sensing system is developed, whicj integrates Inertial Measurement Units (IMU), Knitted Piezoresistive Fabric (KPF) strain sensors, KPF goniometers, EMG electrodes and force sensors into a modular sensor suit designed for stroke patients. In this paper, we describe the complete INTERACTION sensor system. Data from the sensors are captured wirelessly by a software application and stored in a remote secure database for later access and processing via portal technology. Data processing includes a 3D full body reconstruction by means of the Xsens MoCap Engine, providing position and orientation of each body segment (poses). In collaboration with clinicians and engineers, clinical assessment measures were defined and the question of how to present the data on the web portal was addressed. The complete sensing system is fully implemented and is currently being validated. Patients measurements start in June 2014

Asymmetric forces exerted on the horse's back during riding are assumed to have a negative effect on rider-horse interaction and health of the horse. Visualized on a saddle pressure mat they are initially blamed on a non-fitting saddle. The contribution of horse and rider to an asymmetric loading pattern, however, is not well understood. The aim of this study was to investigate the effects of horse and rider asymmetries during stance and in sitting trot on the force distribution on the horse's back using a saddle pressure mat and motion capture analysis simultaneously. Data of 80 horse-rider pairs (HRP) were collected and analyzed using linear (mixed) models to determine the influence of rider and horse variables on asymmetric force distribution. Both, rider and horse variables revealed significant relationships to asymmetric saddle force distribution (P<0.001). During sitting trot, the collapse of the rider in one hip increased the force on the contralateral side and the tilt of the rider's upper body to one side led to more force on the same side of the pressure mat. Analyzing different subsets of data revealed that rider posture as well as horse movements and conformation can cause an asymmetric loading pattern. Since neither horse nor rider movement can be assessed independently during riding, the interpretation of an asymmetric force distribution on the saddle pressure mat remains challenging and all contributing factors (horse, rider, saddle) need to be considered.