Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population. Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations. Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN. Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use

Training of Driving-Related Attentional Performance After Stroke Using a Driving Simulator

The objective of this study was to determine the effect of simulatorbased driving training on attentional performance after stroke. A further analysis of data was conducted from a randomized controlled trial in which the effect of simulator training and cognitive paper and pencil training to improve driving were compared. Performance in divided attention tasks before, during and after 15 hours of simulator-based training of general driving skills in 33 experimental participants were evaluated. Performance in divided attention tasks was assessed during navigation of a 5-km scenario with the divided attention tasks as the only event to respond to and another 13.5-km scenario that contained a good mixture of regular day to day traffic situations. There were significant improvements in mean response time to the divided attention tasks and time to complete the 5-km scenario. Significant decrease in mean response time, number of missed responses, collisions, pedestrians hit, total faults and run time and increase in number of correct responses were found in the 13.5-km scenario. Further analyses showed most improvements in the simulator assessments occurred between preand mid-training. Simulator-based training of driving skills positively impacted attentional performance. Findings in this study suggest that 10 hours of simulatorbased driving training after stroke is sufficient to realize meaningful benefits

Reliability of Upper Limb Pin-Prick Stimulation With Electroencephalography: Evoked Potentials, Spectra and Source Localization

In order for electroencephalography (EEG) with sensory stimuli measures to be used in research and neurological clinical practice, demonstration of reliability is needed. However, this is rarely examined. Here we studied the test-retest reliability of the EEG latency and amplitude of evoked potentials and spectra as well as identifying the sources during pin-prick stimulation. We recorded EEG in 23 healthy older adults who underwent a protocol of pin-prick stimulation on the dominant and non-dominant hand. EEG was recorded in a second session with rest intervals of 1 week. For EEG electrodes Fz, Cz, and Pz peak amplitude, latency and frequency spectra for pin-prick evoked potentials was determined and test-retest reliability was assessed. Substantial reliability ICC scores (0.76–0.79) were identified for evoked potential negative-positive amplitude from the left hand at C4 channel and positive peak latency when stimulating the right hand at Cz channel. Frequency spectra showed consistent increase of low-frequency band activity (< 5 Hz) and also in theta and alpha bands in first 0.25 s. Almost perfect reliability scores were found for activity at both low-frequency and theta bands (ICC scores: 0.81–0.98). Sources were identified in the primary somatosensory and motor cortices in relation to the positive peak using s-LORETA analysis. Measuring the frequency response from the pin-prick evoked potentials may allow the reliable assessment of central somatosensory impairment in the clinical setting

Reliability of Upper Limb Pin-Prick Stimulation With Electroencephalography : Evoked Potentials, Spectra and Source Localization

In order for electroencephalography (EEG) with sensory stimuli measures to be used in research and neurological clinical practice, demonstration of reliability is needed. However, this is rarely examined. Here we studied the test-retest reliability of the EEG latency and amplitude of evoked potentials and spectra as well as identifying the sources during pin-prick stimulation. We recorded EEG in 23 healthy older adults who underwent a protocol of pin-prick stimulation on the dominant and non-dominant hand. EEG was recorded in a second session with rest intervals of 1 week. For EEG electrodes Fz, Cz, and Pz peak amplitude, latency and frequency spectra for pin-prick evoked potentials was determined and test-retest reliability was assessed. Substantial reliability ICC scores (0.76-0.79) were identified for evoked potential negative-positive amplitude from the left hand at C4 channel and positive peak latency when stimulating the right hand at Cz channel. Frequency spectra showed consistent increase of low-frequency band activity (< 5 Hz) and also in theta and alpha bands in first 0.25 s. Almost perfect reliability scores were found for activity at both low-frequency and theta bands (ICC scores: 0.81-0.98). Sources were identified in the primary somatosensory and motor cortices in relation to the positive peak using s-LORETA analysis. Measuring the frequency response from the pin-prick evoked potentials may allow the reliable assessment of central somatosensory impairment in the clinical setting

Short and Predictive Assessment Battery of Fitness-to-Drive After Stroke

The objective of this study was to confirm the accuracy of a previously identified short assessment battery to predict fitness-to-drive after stroke in a new cohort of stroke survivors. This was a prospective study that included 43 (39 males and 4 females) participants who performed the pre-driving assessment that included a standardized on-road test at the Belgian Road Safety Institute in Brussels, Belgium. Participants were on average six months post stroke, not severely physically disabled, possessed valid drivers’ licenses and actively drove prior to stroke onset. Fitness-to-drive decisions made based on performance in 15 tests of a full scale assessment battery were predicted using only scores in three previously identified predictive tests. Performance in the three tests (figure of Rey, visual neglect (lateralized mean reaction time) and on-road test) was used to correctly predict 37 (86%) of the 43 participants’ driving fitness. The sensitivity and specificity of the predictions were 77% and 92% respectively. The outcome of this study shows that the short assessment battery is indeed a good predictor of fitness-to-drive in stroke survivors, especially those without severe deficits

Consensus-Based Core Set of Outcome Measures for Clinical Motor Rehabilitation After Stroke—A Delphi Study

Introduction: Outcome measures are key to tailor rehabilitation goals to the stroke patient’s individual needs and to monitor poststroke recovery. The large number of available outcome measures leads to high variability in clinical use. Currently, an internationally agreed core set of motor outcome measures for clinical application is lacking. Therefore, the goal was to develop such a set to serve as a quality standard in clinical motor rehabilitation poststroke. Methods: Outcome measures for the upper and lower extremities, and activities of daily living (ADL)/stroke-specific outcomes were identified and presented to stroke rehabilitation experts in an electronic Delphi study. In round 1, clinical feasibility and relevance of the outcome measures were rated on a 7-point Likert scale. In round 2, those rated at least as “relevant” and “feasible” were ranked within the body functions, activities, and participation domains of the International Classification of Functioning, Disability, and Health (ICF). Furthermore, measurement time points poststroke were indicated. In round 3, answers were reviewed in reference to overall results to reach final consensus.This work was financially supported by the P & K Pühringer Foundation

Digital Entry-Level Education in Physiotherapy : a Commentary to Inform Post-COVID-19 Future Directions

Open access funding provided by Lund University. © The Author(s) 2021.Currently, the coronavirus disease 2019 (COVID-19) severely influences physiotherapy education which is based mostly on face-to-face teaching. Thus, educators have been compelled to adapt their pedagogical approaches moving to digital education. In this commentary, we debate on digital education highlighting its effectiveness, the users’ perspectives, and its weakness in the context of physiotherapy teaching aimed at informing post-COVID-19 future directions in this educational field. Existing evidence on digital education produced before COVID-19 supports its implementation into entry-level physiotherapy education. However, some challenges (e.g. social inequality and evaluation of students) threaten its applicability in post-COVID-19 era, calling educators to take appropriate actions

Reliability of upper limb pin-prick stimulation with electroencephalography : evoked potentials, spectra and source localization

In order for electroencephalography (EEG) with sensory stimuli measures to be used in research and neurological clinical practice, demonstration of reliability is needed. However, this is rarely examined. Here we studied the test-retest reliability of the EEG latency and amplitude of evoked potentials and spectra as well as identifying the sources during pin-prick stimulation.We recorded EEG in 23 healthy older adults who underwent a protocol of pin-prick stimulation on the dominant and non-dominant hand. EEG was recorded in a second session with rest intervals of 1 week. For EEG electrodes Fz, Cz, and Pz peak amplitude, latency and frequency spectra for pin-prick evoked potentials was determined and test-retest reliability was assessed. Substantial reliability ICC scores (0.76–0.79) were identified for evoked potential negative-positive amplitude from the left hand at C4 channel and positive peak latency when stimulating the right hand at Cz channel. Frequency spectra showed consistent increase of low-frequency band activity (< 5 Hz) and also in theta and alpha bands in first 0.25 s. Almost perfect reliability scores were found for activity at both low-frequency and theta bands (ICC scores: 0.81–0.98). Sources were identified in the primary somatosensory and motor cortices in relation to the positive peak using s-LORETA analysis. Measuring the frequency response from the pin-prick evoked potentials may allow the reliable assessment of central somatosensory impairment in the clinical setting.peer-reviewe

Technology-supported sitting balance therapy versus usual care in the chronic stage after stroke : a pilot randomized controlled trial

Background: Technology development for sitting balance therapy and trunk rehabilitation is scarce. Hence, intensive one-to-one therapist-patient training is still required. We have developed a novel rehabilitation prototype, specifically aimed at providing sitting balance therapy. We investigated whether technology-supported sitting balance training was feasible and safe in chronic stroke patients and we determined whether clinical outcomes improved after a four-week programme, compared with usual care. Methods: In this parallel-group, assessor-blinded, randomized controlled pilot trial, we divided first-event chronic stroke participants into two groups. The experimental group received usual care plus additional therapy supported by rehabilitation technology, consisting of 12 sessions of 50 min of therapy over four weeks. The control group received usual care only. We assessed all participants twice pre-intervention and once post-intervention. Feasibility and safety were descriptively analysed. Between-group analysis evaluated the pre-to-post differences in changes in motor and functional outcomes. Results: In total, 30 participants were recruited and 29 completed the trial (experimental group: n = 14; control group: n = 15). There were no between-group differences at baseline. Therapy was evaluated as feasible by participants and therapist. There were no serious adverse events during sitting balance therapy. Changes in clinical outcomes from pre- to post-intervention demonstrated increases in the experimental than in the control group for: sitting balance and trunk function, evaluated by the Trunk Impairment Scale (mean points score (SD) 7.07 (1.69) versus 0.33 (2.35); p < 0.000); maximum gait speed, assessed with the 10 Metre Walk Test (mean gait speed 0.16 (0.16) m/s versus 0.06 (0.06) m/s; p = 0.003); and functional balance, measured using the Berg balance scale (median points score (IQR) 4.5 (5) versus 0 (4); p = 0.014). Conclusions: Technology-supported sitting balance training in persons with chronic stroke is feasible and safe. A four-week, 12-session programme on top of usual care suggests beneficial effects for trunk function, maximum gait speed and functional balance