A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

This article presents the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls

Using a microprocessor knee (C-Leg) with appropriate foot transitioned individuals with dysvascular transfemoral amputations to higher performance levels: a longitudinal randomized clinical trial

Article evaluating whether advanced prostheses can provide better safety and performance capabilities to maintain and improve quality of life in individuals who are predominantly designated MFCL level K2. This study used a 13 month longitudinal clinical trial to determine the benefits of using a C-Leg and 1M10 foot in individuals at K2 level with transfemoral amputation due to vascular disease

Budget impact analysis of robotic exoskeleton use for locomotor training following spinal cord injury in four SCI Model Systems

Background We know little about the budget impact of integrating robotic exoskeleton over-ground training into therapy services for locomotor training. The purpose of this study was to estimate the budget impact of adding robotic exoskeleton over-ground training to existing locomotor training strategies in the rehabilitation of people with spinal cord injury. Methods A Budget Impact Analysis (BIA) was conducted using data provided by four Spinal Cord Injury (SCI) Model Systems rehabilitation hospitals. Hospitals provided estimates of therapy utilization and costs about people with spinal cord injury who participated in locomotor training in the calendar year 2017. Interventions were standard of care walking training including body-weight supported treadmill training, overground training, stationary robotic systems (i.e., treadmill-based robotic gait orthoses), and overground robotic exoskeleton training. The main outcome measures included device costs, training costs for personnel to use the device, human capital costs of locomotor training, device demand, and the number of training sessions per person with SCI. Results Robotic exoskeletons for over-ground training decreased hospital costs associated with delivering locomotor training in the base case analysis. This analysis assumed no difference in intervention effectiveness across locomotor training strategies. Providing robotic exoskeleton overground training for 10% of locomotor training sessions over the course of the year (range 226–397 sessions) results in decreased annual locomotor training costs (i.e., net savings) between $1114 to$4784 per annum. The base case shows small savings that are sensitive to parameters of the BIA model which were tested in one-way sensitivity analyses, scenarios analyses, and probability sensitivity analyses. The base case scenario was more sensitive to clinical utilization parameters (e.g., how often devices sit idle and the substitution of high cost training) than device-specific parameters (e.g., robotic exoskeleton device cost or device life). Probabilistic sensitivity analysis simultaneously considered human capital cost, device cost, and locomotor device substitution. With probabilistic sensitivity analysis, the introduction of a robotic exoskeleton only remained cost saving for one facility. Conclusions Providing robotic exoskeleton for over-ground training was associated with lower costs for the locomotor training of people with SCI in the base case analyses. The analysis was sensitive to parameter assumptions

Analysis of EMG During Clonus Using Wavelets

Involuntary muscle contractions (spasms) are a major secondary consequence of spinal cord injury. These spasms disrupt mobility and the ability to perform daily activities. The rhythmic repetitive muscle contractions of clonus are one kind of spasm. In this study an algorithm was developed to automatically detect the start and end times of EMG bursts during clonus. These measures were used to calculate the duration of EMG bursts, clonus frequency and the intensity (root mean square) of each EMG burst, parameters that characterize clonus. This algorithm relied on the technique of intensity analysis (Von Tscharner 2000). Filters were created by non-linearly scaling a Mother (Morlet) wavelet to produce envelopes of the EMG in different frequency bands. The intermediate frequency band (80-190 Hz) enveloped the EMG best and was used to detect the EMG bursts during clonus. To detect the EMG bursts, an intensity threshold and time separation threshold were imposed on the algorithm to eliminate multiple peaks caused by the baseline EMG, motor units or EMG changes. Window regions were extended between the midpoints of identified EMG peaks then resized to 50 ms on either side of each identified EMG peak. The start and end times of EMG bursts were at 5% and 95% of the energy contained in a window region, respectively. A motor unit threshold constraint was used to eliminate motor unit potentials at the beginning and end of clonus. The algorithm output from 31 spasms in long term (24 hr) EMG data recorded from 8 paralyzed leg muscles of 7 subjects with a chronic cervical spinal cord injury were compared to that generated by two independent human operators. The algorithm was as good as a human operator at identifying EMG bursts (p = 0.946), clonus frequency (intra class correlation coefficient (p = 0.949), contraction intensity (p = 0.997) and the durations of each burst of EMG during clonus (p = 0.852). On average the algorithm was 574 (SE 238) times faster than manual analysis by two people (p <= 0.001). Analysis of clonus in one 24 hour dataset from the right medial gastrocnemius muscle with the algorithm showed that clonus was more prevalent and stronger during awake versus sleep time. This algorithm can be used to analyze long term recordings accurately with limited user intervention. The algorithm may also be a prospective diagnostic tool to judge the effectiveness of interventions such as drugs like baclofen that are used to mitigate clonus.</p

Automatic analysis of EMG during clonus

► Novel algorithm to automatically characterize clonus in long-term EMG records. ► Wavelets were scaled non-linearly to extract time–frequency information from EMG. ► Threshold and time constraints improved algorithm accuracy. ► Robust algorithm performance across muscles, subjects, and time. ► Potential research tool to facilitate analysis of involuntary muscle contractions. Clonus can disrupt daily activities after spinal cord injury. Here an algorithm was developed to automatically detect contractions during clonus in 24h electromyographic (EMG) records. Filters were created by non-linearly scaling a Mother (Morlet) wavelet to envelope the EMG using different frequency bands. The envelope for the intermediate band followed the EMG best (74.8–193.9Hz). Threshold and time constraints were used to reduce the envelope peaks to one per contraction. Energy in the EMG was measured 50ms either side of each envelope (contraction) peak. Energy values at 5% and 95% maximal defined EMG start and end time, respectively. The algorithm was as good as a person at identifying contractions during clonus (p=0.946, n=31 spasms, 7 subjects with cervical spinal cord injury), and marking start and end times to determine clonus frequency (intra class correlation coefficient, α: 0.949), contraction intensity using root mean square EMG (α: 0.997) and EMG duration (α: 0.852). On average the algorithm was 574 times faster than manual analysis performed independently by two people (p≤0.001). This algorithm is an important tool for characterization of clonus in long-term EMG records

Corticospinal Reorganization after Locomotor Training in a Person with Motor Incomplete Paraplegia

Activity-dependent plasticity as a result of reorganization of neural circuits is a fundamental characteristic of the central nervous system that occurs simultaneously in multiple sites. In this study, we established the effects of subthreshold transcranial magnetic stimulation (TMS) over the primary motor cortex region on the tibialis anterior (TA) long-latency flexion reflex. Neurophysiological tests were conducted before and after robotic gait training in one person with a motor incomplete spinal cord injury (SCI) while at rest and during robotic-assisted stepping. The TA flexion reflex was evoked following nonnociceptive sural nerve stimulation and was conditioned by TMS at 0.9 TA motor evoked potential resting threshold at conditioning-test intervals that ranged from 70 to 130 ms. Subthreshold TMS induced a significant facilitation on the TA flexion reflex before training, which was reversed to depression after training with the subject seated at rest. During stepping, corticospinal facilitation of the flexion reflex at early and midstance phases before training was replaced with depression at early and midswing followed by facilitation at late swing after training. These results constitute the first neurophysiologic evidence that locomotor training reorganizes the cortical control of spinal interneuronal circuits that generate patterned motor activity, modifying spinal reflex function, in the chronic lesioned human spinal cord

Variables influencing wearable sensor outcome estimates in individuals with stroke and incomplete spinal cord injury: a pilot investigation validating two research grade sensors

Abstract Background Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living. In this context, accurate measurement of physical activity estimates from these sensors are vital. However, wearable sensor manufacturers generally only provide standard proprietary algorithms based off of healthy individuals to estimate physical activity metrics which may lead to inaccurate estimates in population with neurological impairment like stroke and incomplete spinal cord injury (iSCI). The main objective of this cross-sectional investigation was to evaluate the validity of physical activity estimates provided by standard proprietary algorithms for individuals with stroke and iSCI. Two research grade wearable sensors used in clinical settings were chosen and the outcome metrics estimated using standard proprietary algorithms were validated against designated golden standard measures (Cosmed K4B2 for energy expenditure and metabolic equivalent and manual tallying for step counts). The influence of sensor location, sensor type and activity characteristics were also studied. Methods 28 participants (Healthy (n = 10); incomplete SCI (n = 8); stroke (n = 10)) performed a spectrum of activities in a laboratory setting using two wearable sensors (ActiGraph and Metria-IH1) at different body locations. Manufacturer provided standard proprietary algorithms estimated the step count, energy expenditure (EE) and metabolic equivalent (MET). These estimates were compared with the estimates from gold standard measures. For verifying validity, a series of Kruskal Wallis ANOVA tests (Games-Howell multiple comparison for post-hoc analyses) were conducted to compare the mean rank and absolute agreement of outcome metrics estimated by each of the devices in comparison with the designated gold standard measurements. Results The sensor type, sensor location, activity characteristics and the population specific condition influences the validity of estimation of physical activity metrics using standard proprietary algorithms. Conclusions Implementing population specific customized algorithms accounting for the influences of sensor location, type and activity characteristics for estimating physical activity metrics in individuals with stroke and iSCI could be beneficial