The telerehabilitation approach in VR supported balance training improved balance in stroke patients and had similar effect on patients' postural functional improvement as conventional balance training in clinical settings. However, when balance training is continued on patient's home instead of the hospital, it would eventually decrease the number of outpatients' visits, reduce related costs and enable treatment of larger number of patients.

BackgroundMobile health monitoring using wearable sensors is a growing area of interest. As the world’s population ages and locomotor capabilities decrease, the ability to report on a person’s mobility activities outside a hospital setting becomes a valuable tool for clinical decision-making and evaluating healthcare interventions. Smartphones are omnipresent in society and offer convenient and suitable sensors for mobility monitoring applications. To enhance our understanding of human activity recognition (HAR) system performance for able-bodied and populations with gait deviations, this research evaluated a custom smartphone-based HAR classifier on fifteen able-bodied participants and fifteen participants who suffered a stroke.MethodsParticipants performed a consecutive series of mobility tasks and daily living activities while wearing a BlackBerry Z10 smartphone on their waist to collect accelerometer and gyroscope data. Five features were derived from the sensor data and used to classify participant activities (decision tree). Sensitivity, specificity and F-scores were calculated to evaluate HAR classifier performance.ResultsThe classifier performed well for both populations when differentiating mobile from immobile states (F-score > 94 %). As activity recognition complexity increased, HAR system sensitivity and specificity decreased for the stroke population, particularly when using information derived from participant posture to make classification decisions.ConclusionsHuman activity recognition using a smartphone based system can be accomplished for both able-bodied and stroke populations; however, an increase in activity classification complexity leads to a decrease in HAR performance with a stroke population. The study results can be used to guide smartphone HAR system development for populations with differing movement characteristics.

We aimed to verify by Rasch analysis whether the Mini-BESTest, a balance measure, confirms its main psychometric properties in patients with subacute stroke undergoing rehabilitation in three different countries (Slovenia, Croatia, and Italy), and to examine the stability of item hierarchy and difficulty across the three national versions through a differential item functioning analysis. We investigated 159 patients with subacute stroke consecutively admitted to three rehabilitation facilities after screening for an intensive, tailored rehabilitation program. Balance function was tested within 36 h from admission and after ∼25 days. As no differential item functioning was found between admission and discharge data or among countries, all data were pooled. Rasch criteria for the functioning of rating scale categories were fulfilled. In terms of internal construct validity, all items except item #14 (Cognitive Get Up & Go; infit value=1.42) showed an acceptable fit to the Rasch model. The patient ability-item difficulty matching was very good. Reliability indices were high. The Principal Component Analysis of standardized residuals confirmed the unidimensionality of the test. On the basis of the item calibration, raw scores of the Mini-BESTest were transformed into linear estimates of dynamic balance and six statistically detectable levels of balance ability were defined. Good psychometric features of the Mini-BESTest were confirmed. The three different national versions showed stability in item hierarchy, indicating equivalence of their cross-cultural adaptations. Problems with item #14 in these patients warrant further study.

The aim of the study was to compare the efficacy of balance training in a balance trainer, a newly developed mechanical device for training balance, with conventional balance training in subacute stroke patients. This was a randomized controlled study. Fifty participants met the inclusion criteria and 39 finished the study. The participants were randomly divided into control and balance trainer groups. The first had conventional balance training while the second trained balance in the balance trainer. All the participants trained balance 20 min per day, 5 days per week for 4 weeks and had additional 25 min of physiotherapy. Balance was assessed by the Berg Balance Scale, one-leg standing, Timed Up and Go (TUG) Test and 10 m walk. There was significant improvement in Berg Balance Scale (P<0.001), TUG (P<0.001) and 10 m walk (P=0.001) in both the groups, whereas no differences were found in any of these measures between the two groups either regarding overall average level or regarding average improvement. Both the groups improved significantly in standing on the healthy (P=0.001) as well as the impaired lower limb (P=0.005), whereby no significant differences were observed between the groups. Within both groups, significantly fewer subjects needed assistance of a physiotherapist for the 10 m walk and the TUG test at the end than at the beginning of the study (P=0.016). It can be concluded that both conventional balance training and training balance in the balance trainer equally improved balance in subacute stroke patients. The balance trainer cannot replace a physiotherapist but it is a safe and efficient supplementary method.