Arm impairment and walking speed explain real-life activity of the affected arm and leg after stroke.

OBJECTIVE: To determine to what extent accelerometer-based arm, leg and trunk activity is associated with sensorimotor impairments, walking capacity and other factors in subacute stroke. DESIGN: Cross-sectional study. PATIENTS: Twenty-six individuals with stroke (mean age 55.4 years, severe to mild motor impairment). METHODS: Data on daytime activity were collected over a period of 4 days from accelerometers placed on the wrists, ankles and trunk. A forward stepwise linear regression was used to determine associations between free-living activity, clinical and demographic variables. RESULTS: Arm motor impairment (Fugl-Meyer Assessment) and walking speed explained more than 60% of the variance in daytime activity of the more-affected arm, while walking speed alone explained 60% of the more-affected leg activity. Activity of the less-affected arm and leg was associated with arm motor impairment (R2 = 0.40) and independence in walking (R2 = 0.59). Arm activity ratio was associated with arm impairment (R2 = 0.63) and leg activity ratio with leg impairment (R2 = 0.38) and walking speed (R2 = 0.27). Walking-related variables explained approximately 30% of the variance in trunk activity. CONCLUSION: Accelerometer-based free-living activity is dependent on motor impairment and walking capacity. The most relevant activity data were obtained from more-affected limbs. Motor impairment and walking speed can provide some information about real-life daytime activity levels.

Comparison of accelerometer-based arm, leg and trunk activity at weekdays and weekends during subacute inpatient rehabilitation after stroke.

OBJECTIVE: To determine whether there are differences in arm, leg and trunk activity measured by acceleration between weekdays and weekends in people undergoing rehabilitation in the subacute stage after stroke. DESIGN: Cross-sectional study. PATIENTS: Twenty-eight individuals with stroke (mean age 55.4 years; severe to mild impairment) and 10 healthy controls. METHODS: A set of 5 3-axial accelerometers were used on the trunk, wrists and ankles during 2 48-h sessions at weekdays and over a weekend. Day-time acceleration raw data were expressed as the signal magnitude area. Asymmetry between the affected and less-affected limb was calculated as a ratio. RESULTS: Participants with stroke used their both arms and legs less at weekends than on weekdays (p< 0.05, effect size 0.32-0.57). Asymmetry between the affected and less-affected arm was greater at weekends (p < 0.05, effect size 0.32). All activity measures, apart from the less-affected arm on weekdays, were lower in stroke compared with controls (p < 0.05, effect size 0.4-0.8). No statistically significant differences were detected between weekday and weekend activity for the control group. One-third of participants perceived the trunk sensor as inconvenient to wear. CONCLUSION: Increased focus needs to be applied on activities carried out during weekends at rehabilitation wards.

Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study.

PURPOSE: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets. METHODS: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set. RESULTS: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h). CONCLUSIONS: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management.

An upper body garment with integrated sensors for people with neurological disorders - early development and evaluation.

BACKGROUND: In neurology and rehabilitation the primary interest for using wearables is to supplement traditional patient assessment and monitoring in hospital settings with continuous data collection at home and in community settings. The aim of this project was to develop a novel wearable garment with integrated sensors designed for continuous monitoring of physiological and movement related variables to evaluate progression, tailor treatments and improve diagnosis in epilepsy, Parkinson's disease and stroke. In this paper the early development and evaluation of a prototype designed to monitor movements and heart rate is described. An iterative development process and evaluation of an upper body garment with integrated sensors included: identification of user needs, specification of technical and garment requirements, garment development and production as well as evaluation of garment design, functionality and usability. The project is a multidisciplinary collaboration with experts from medical, engineering, textile, and material science within the wearITmed consortium. The work was organized in regular meetings, task groups and hands-on workshops. User needs were identified using results from a mixed-methods systematic review, a focus group study and expert groups. Usability was evaluated in 19 individuals (13 controls, 6 patients with Parkinson's disease) using semi-structured interviews and qualitative content analysis. RESULTS: The garment was well accepted by the users regarding design and comfort, although the users were cautious about the technology and suggested improvements. All electronic components passed a washability test. The most robust data was obtained from accelerometer and gyroscope sensors while the electrodes for heart rate registration were sensitive to motion artefacts. The algorithm development within the wearITmed consortium has shown promising results. CONCLUSIONS: The prototype was accepted by the users. Technical improvements are needed, but preliminary data indicate that the garment has potential to be used as a tool for diagnosis and treatment selection and could provide added value for monitoring seizures in epilepsy, fluctuations in PD and activity levels in stroke. Future work aims to improve the prototype further, develop algorithms, and evaluate the functionality and usability in targeted patient groups. The potential of incorporating blood pressure and heart-rate variability monitoring will also be explored.

Non-destructive Phenotypic Analysis of Early Stage Tree Seedling Growth Using an Automated Stereovision Imaging Method.

A plant phenotyping approach was applied to evaluate growth rate of containerized tree seedlings during the precultivation phase following seed germination. A simple and affordable stereo optical system was used to collect stereoscopic red-green-blue (RGB) images of seedlings at regular intervals of time. Comparative analysis of these images by means of a newly developed software enabled us to calculate (a) the increments of seedlings height and (b) the percentage greenness of seedling leaves. Comparison of these parameters with destructive biomass measurements showed that the height traits can be used to estimate seedling growth for needle-leaved plant species whereas the greenness trait can be used for broad-leaved plant species. Despite the need to adjust for plant type, growth stage and light conditions this new, cheap, rapid, and sustainable phenotyping approach can be used to study large-scale phenome variations due to genome variability and interaction with environmental factors.