Image_1_Longitudinal Walking Analysis in Hemiparetic Patients Using Wearable Motion Sensors: Is There Convergence Between Body Sides?.pdf

<p>Background: Longitudinal movement parameter analysis of hemiparetic patients over several months could reveal potential recovery trends and help clinicians adapting therapy strategies to maximize recovery outcome. Wearable sensors offer potential for day-long movement recordings in realistic rehabilitation settings including activities of daily living, e.g., walking. The measurement of walking-related movement parameters of affected and non-affected body sides are of interest to determine mobility and investigate recovery trends.</p><p>Methods: By comparing movement of both body sides, recovery trends across the rehabilitation duration were investigated. We derived and validated selected walking segments from free-living, day-long movement by using rules that do not require data-based training or data annotations. Automatic stride segmentation using peak detection was applied to walking segments. Movement parameters during walking were extracted, including stride count, stride duration, cadence, and sway. Finally, linear regression models over each movement parameter were derived to forecast the moment of convergence between body sides. Convergence points were expressed as duration and investigated in a patient observation study.</p><p>Results: Convergence was analyzed in walking-related movement parameters in an outpatient study including totally 102 full-day recordings of inertial movement data from 11 hemiparetic patients. The recordings were performed over several months in a day-care centre. Validation of the walking extraction method from sensor data yielded sensitivities up to 80 % and specificity above 94 % on average. Comparison of automatically and manually derived movement parameters showed average relative errors below 6 % between affected and non-affected body sides. Movement parameter variability within and across patients was observed and confirmed by case reports, reflecting individual patient behavior.</p><p>Conclusion: Convergence points were proposed as intuitive metric, which could facilitate training personalization for patients according to their individual needs. Our continuous movement parameter extraction and analysis, was feasible for realistic, day-long recordings without annotations. Visualizations of movement parameter trends and convergence points indicated that individual habits and patient therapies were reflected in walking and mobility. Context information of clinical case reports supported trend and convergence interpretation. Inconsistent convergence point estimation suggested individually varying deficiencies. Long-term recovery monitoring using convergence points could support patient-specific training strategies in future remote rehabilitation.</p

Study flow chart.

<p>BL = baseline assessment; <i>T</i>₀ = pre-test, <i>T</i>₁ = post-test, <i>T</i>₂ = 4-week follow-up. SMT = sensorimotor training group; SLT = sub-effective low-intensity endurance training group; PT = standard physiotherapy.</p

Mean, standard deviation and range values for characteristics of the study population.

<p>Mean, standard deviation and range values for characteristics of the study population.</p

Schematic representation of the defined segment angles.

<p>Θ_<i>N</i> = Neck angle; Θ_<i>L</i> = Lumbar angle; Θ_<i>H</i> = Hip angle; Θ_<i>K</i> = Knee angle; Θ_<i>A</i> = Ankle angle; Θ_<i>F</i> = Foot angle; Marker positions (from head to toe): corner of the eye (orbital process of the zygomatic bone), acromion, anterior superior iliac spine, greater trochanter, lateral condyle of femur, lateral malleolus, 1st metatarsal bone.</p

Main results of primary and secondary outcomes at 4 measurement events.

<p>Main results of primary and secondary outcomes at 4 measurement events.</p

Postural sensorimotor training versus sham exercise in physiotherapy of patients with chronic non-specific low back pain: An exploratory randomised controlled trial

<div><p>Sensorimotor training (SMT) is popularly applied as exercise in rehabilitation settings, particularly for musculoskeletal pain. With insufficient evidence on its effect on pain and function, this exploratory randomised controlled trial investigated the potential effects of SMT in rehabilitation of chronic non-specific low back pain. Two arms received 9x30 minutes physiotherapy with added interventions: The experimental arm received 15 minutes of postural SMT while the comparator arm performed 15 minutes of added sub-effective low-intensity training. A treatment blinded tester assessed outcomes at baseline 2–4 days prior to intervention, pre- and post-intervention, and at 4-week follow-up. Main outcomes were pain and functional status assessed with a 0–100mm visual analogue scale and the Oswestry Disability Questionnaire. Additionally, postural control was analysed using a video-based tracking system and a pressure plate during perturbed stance. Robust, nonparametric multivariate hypothesis testing was performed. 22 patients (11 females, aged 32 to 75 years) with mild to moderate chronic pain and functional limitations were included for analysis (11 per arm). At post-intervention, average values of primary outcomes improved slightly, but not to a clinically relevant or statistically significant extent. At 4-week follow-up, there was a significant improvement by 12 percentage points (pp) on the functional status questionnaire in the SMT-group (95% confidence intervall (CI) = 5.3pp to 17.7pp, <i>p</i> < 0.001) but not in the control group (4 pp improvement, CI = 11.8pp to 19.2pp). However, group-by-time interaction effects for functional status (Q = 3.3, 19 p = 0.07) and pain (Q = 0.84, p = 0.51) were non-significant. Secondary kinematic outcomes did not change over time in either of the groups. Despite significant improvement of functional status after SMT, overall findings of this exploratory study suggest that SMT provides no added benefit for pain reduction or functional improvement in patients with moderate chronic non-specific low back pain.</p><p><b><i>Trial registration</i></b>: ClinicalTrials.gov <a href="https://clinicaltrials.gov/ct2/show/NCT02304120" target="_blank">NCT02304120</a> and related study protocol, DOI: <a href="https://doi.org/10.1186/1471-2474-15-382" target="_blank">10.1186/1471-2474-15-382</a>.</p></div

Principal component analysis of the involved segments.

<p>Principal component analysis of the involved segments.</p

Group comparison of variance components.

<p>Across trials mean variance components during the first second of the active response phase after platform release. CG = control group; CNLBP = Chronic non-specific low back pain group.</p

Mean and range values for characteristics of the study population.

<p>Mean and range values for characteristics of the study population.</p

Illustration of the measurement setup.

<p>Illustration of the measurement setup.</p