Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients.

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

Predicting Duration of Outpatient Physical Therapy Episodes for Individuals with Spinal Cord Injury Based on Locomotor Training Strategy.

OBJECTIVE: To characterize individuals with spinal cord injuries (SCI) who use outpatient physical therapy or community wellness services for locomotor training and predict the duration of services, controlling for demographic, injury, quality of life, and service and financial characteristics. We explore how the duration of services is related to locomotor strategy. DESIGN: Observational study of participants at 4 SCI Model Systems centers with survival. Weibull regression model to predict the duration of services. SETTING: Rehabilitation and community wellness facilities at 4 SCI Model Systems centers. PARTICIPANTS: Eligibility criteria were SCI or dysfunction resulting in motor impairment and the use of physical therapy or community wellness programs for locomotor/gait training. We excluded those who did not complete training or who experienced a disruption in training greater than 45 days. Our sample included 62 participants in conventional therapy and 37 participants in robotic exoskeleton training. INTERVENTIONS: Outpatient physical therapy or community wellness services for locomotor/gait training. MAIN OUTCOME MEASURES: SCI characteristics (level and completeness of injury) and the duration of services from medical records. Self-reported perceptions of SCI consequences using the SCI-Functional Index for basic mobility and SCI-Quality of Life measurement system for bowel difficulties, bladder difficulties, and pain interference. RESULTS: After controlling for predictors, the duration of services for the conventional therapy group was an average of 63% longer than for the robotic exoskeleton group, however each visit was 50% shorter in total time. Men had an 11% longer duration of services than women had. Participants with complete injuries had a duration of services that was approximately 1.72 times longer than participants with incomplete injuries. Perceived improvement was larger in the conventional group. CONCLUSIONS: Locomotor/gait training strategies are distinctive for individuals with SCI using a robotic exoskeleton in a community wellness facility as episodes are shorter but individual sessions are longer. Participants' preferences and the ability to pay for ongoing services may be critical factors associated with the duration of outpatient services.

Wearable airbag technology and machine learned models to mitigate falls after stroke.

BACKGROUND: Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population. METHODS: We collected data from a wearable airbag's inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15 control) history of stroke while performing a series of falls (842 falls total) and non-falls (961 non-falls total) in a laboratory setting. A leave-one-subject-out crossvalidation was used to compare the performance of two identical machine learned models (adaptive boosting classifier) trained on cohort-dependent data (control or stroke) to pre-detect falls in the stroke cohort. RESULTS: The average performance of the model trained on stroke data (recall = 0.905, precision = 0.900) had statistically significantly better recall (P = 0.0035) than the model trained on control data (recall = 0.800, precision = 0.944), while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior-posterior (AP) falls (stroke-trained model's F1-score was 35% higher, P = 0.019). Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the AUC (Area under the receiver operating characteristic) for classifying AP falls for both models (P < 0.04). Preliminary analysis suggests that users with more severe stroke impairments benefit further from a stroke-trained model. The optimal lead time (time interval pre-impact to detect falls) differed between control- and stroke-trained models. CONCLUSIONS: These results demonstrate the importance of population sensitivity, non-falls data, and optimal lead time for machine learned pre-impact fall detection specific to stroke. Existing fall mitigation technologies should be challenged to include data of neurologically impaired individuals in model development to adequately detect falls in other high fall risk populations. Trial registration https://clinicaltrials.gov/ct2/show/NCT05076565 ; Unique Identifier: NCT05076565. Retrospectively registered on 13 October 2021.

Modular hip exoskeleton improves walking function and reduces sedentary time in community-dwelling older adults.

BACKGROUND: Despite the benefits of physical activity for healthy physical and cognitive aging, 35% of adults over the age of 75 in the United States are inactive. Robotic exoskeleton-based exercise studies have shown benefits in improving walking function, but most are conducted in clinical settings with a neurologically impaired population. Emerging technology is starting to enable easy-to-use, lightweight, wearable robots, but their impact in the otherwise healthy older adult population remains mostly unknown. For the first time, this study investigates the feasibility and efficacy of using a lightweight, modular hip exoskeleton for in-community gait training in the older adult population to improve walking function. METHODS: Twelve adults over the age of 65 were enrolled in a gait training intervention involving twelve 30-min sessions using the Gait Enhancing and Motivating System for Hip in their own senior living community. RESULTS: Performance-based outcome measures suggest clinically significant improvements in balance, gait speed, and endurance following the exoskeleton training, and the device was safe and well tolerated. Gait speed below 1.0 m/s is an indicator of fall risk, and two out of the four participants below this threshold increased their self-selected gait speed over 1.0 m/s after intervention. Time spent in sedentary behavior also decreased significantly. CONCLUSIONS: This intervention resulted in greater improvements in speed and endurance than traditional exercise programs, in significantly less time. Together, our results demonstrated that exoskeleton-based gait training is an effective intervention and novel approach to encouraging older adults to exercise and reduce sedentary time, while improving walking function. Future work will focus on whether the device can be used independently long-term by older adults as an everyday exercise and community-use personal mobility device. Trial registration This study was retrospectively registered with ClinicalTrials.gov (ID: NCT05197127).

Microprocessor Controlled Knee Ankle Foot Orthosis (KAFO) vs Stance Control vs Locked KAFO: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the potential of a microprocessor swing and stance controlled knee-ankle-foot orthosis (MPO) to improve balance, functional mobility, and quality of life in individuals with lower-extremity impairments as compared to a stance-control-orthosis (SCO) and conventional knee-ankle-foot orthosis (KAFO) over a use-period of a month. DESIGN: Randomized crossover study. SETTING: Ambulatory research laboratory and home and community for community-dwelling adults. PARTICIPANTS: Persons (N=18) who actively used a unilateral KAFO or SCO for impairments due to neurologic or neuromuscular disease, orthopedic disease, or trauma. INTERVENTION: Participants were trained to acclimate and use SCO and MPO. MAIN OUTCOME MEASURES: The 6-minute walk test (6MWT), 10-m walk test, Berg Balance Scale (BBS), functional gait assessment (FGA), hill assessment index, stair assessment index (SAI), Five Times Sit to Stand Test, crosswalk test, Modified Falls Efficacy Scale, Orthotic and Prosthetic User's Survey (OPUS), and World Health Organization Quality of Life (WHQOL)-BREF Scale. RESULTS: Significant changes were observed in participants' self-selected gait speed (P=.023), BBS (P=.01), FGA (P=.002), and SAI (P<.001) between baseline and post-MPO assessment. Similar significant differences were seen when comparing post-MPO with post-SCO data. During the 6MWT, persons using the MPO walked significantly longer (P=.013) than when using their baseline device. Participants reported higher quality of life scores in the OPUS (P=.02) and physical health domain of the WHOQOL-BREF (P=.037) after using the MPO. Participants reported fewer falls when wearing the MPO (5) versus an SCO (38) or locked KAFO (15). CONCLUSIONS: The MPO may contribute to improved quality of life and health status of persons with lower-extremity impairments by providing the ability to have better walking speed, endurance, and functional balance.

Clinician Perceptions of Robotic Exoskeletons for Locomotor Training After Spinal Cord Injury: A Qualitative Approach.

OBJECTIVE: To describe the experiences of clinicians who have used robotic exoskeletons in their practice and acquire information that can guide clinical decisions and training strategies related to robotic exoskeletons. DESIGN: Qualitative, online survey study, and 4 single-session focus groups followed by thematic analysis to define themes. SETTING: Focus groups were conducted at 3 regional rehabilitation hospitals and 1 Veteran's Administration (VA) Medical Center. PARTICIPANTS: Clinicians (N=40) reported their demographic characteristics and clinical experience using robotic exoskeletons. Twenty-nine clinicians participated in focus groups at regional hospitals that use robotic exoskeletons, as well as 1 VA Medical Center. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Clinicians' preferences, experiences, training strategies, and clinical decisions on how robotic exoskeleton devices are used with Veterans and civilians with spinal cord injury. RESULTS: Clinicians had an average of 3 years of experience using exoskeletons in clinical and research settings. Major themes emerging from focus group discussions included appropriateness of patient goals, patient selection criteria, realistic patient expectations, patient and caregiver training for use of exoskeletons, perceived benefits, preferences regarding specific exoskeletons, and device limitations and therapy recommendations. CONCLUSIONS: Clinicians identified benefits of exoskeleton use including decreased physical burden and fatigue while maximizing patient mobility, increased safety of clinicians and patients, and expanded device awareness and preferences. Suitability of exoskeletons for patients with various characteristics and managing expectations were concerns. Clinicians identified research opportunities as technology continues to advance toward safer, lighter, and hands-free devices.

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

BACKGROUND: Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. METHODS: The system uses the smartphone's accelerometer and gyroscope to monitor the participants' motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller's location and activity before the fall. RESULTS: In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles. CONCLUSIONS: The system's performance shows the potential of using smartphones for fall detection and notification in real-life. 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.

BACKGROUND: Individuals with transfemoral amputations who are considered to be limited community ambulators are classified as Medicare functional classification (MFCL) level K2. These individuals are usually prescribed a non-microprocessor controlled knee (NMPK) with an appropriate foot for simple walking functions. However, existing research suggests that these individuals can benefit from using a microprocessor controlled knee (MPK) and appropriate foot for their ambulation, but cannot obtain one due to insurance policy restrictions. With a steady increase in older adults with amputations due to vascular conditions, it is critical to evaluate 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. To decipher this we conducted 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. This longitudinal clinical trial incorporated recommendations prescribed by the lower limb prosthesis workgroup to design a study that can add evidence to improve reimbursement policy through clinical outcomes using an MPK in K2 level individuals with transfemoral amputation who were using an NMPK for everyday use. METHODS: Ten individuals (mean age: 63 ± 9 years) with unilateral transfemoral amputation due to vascular conditions designated as MFCL K2 participated in this longitudinal crossover randomized clinical trial. Baseline outcomes were collected with their current prosthesis. Participants were then randomized to one of two groups, either an intervention with the MPK with a standardized 1M10 foot or their predicate NMPK with a standardized 1M10 foot. On completion of the first intervention, participants crossed over to the next group to complete the study. Each intervention lasted for 6 months (3 months of acclimation and 3 months of take-home trial to monitor home use). At the end of each intervention, clinical outcomes and self-reported outcomes were collected to compare with their baseline performance. A generalized linear model ANOVA was used to compare the performance of each intervention with respect to their own baseline. RESULTS: Statistically significant and clinically meaningful improvements were observed in gait performance, safety, and participant-reported measures when using the MPK C-Leg + 1M10 foot. Most participants were able to achieve higher clinical scores in gait speed, balance, self-reported mobility, and fall safety, while using the MPK + 1M10 combination. The improvement in scores were within range of scores achieved by individuals with K3 functional level as reported in previous studies. CONCLUSIONS: Individuals with transfemoral amputation from dysvascular conditions designated MFCL level K2 benefited from using an MPK + appropriate foot. The inference and evidence from this longitudinal clinical trial will add to the knowledgebase related to reimbursement policy-making. Trial registration This study is registered on clinical trials.gov with the study title "Functional outcomes in dysvascular transfemoral amputees" and the associated ClinicalTrials.gov Identifier: NCT01537211. The trial was retroactively registered on February 7, 2012 after the first participant was enrolled.

The impact of a yoga-based physical therapy group for individuals with traumatic brain injury: results from a pilot study.

OBJECTIVE: To compare the impacts of yoga-based physical therapy versus a seated rest within the context of standard rehabilitation practice on sleep, heart rate variability (HRV), anxiety, and fatigue during acute traumatic brain injury (TBI) rehabilitation. METHODS: Eleven individuals participated in this crossover study involving the following interventions in a randomized order: group yoga-based physical therapy (YPT), conventional physical therapy (CPT), and group seated rest in a relaxing environment (SR). HRV and self-reported anxiety and fatigue were measured immediately before and after each group, and sleep after each condition and at baseline. Data was analyzed using generalized linear mixed models with repeated measures. RESULTS: The interaction between time and treatment was statistically significant (p = .0203). For the SR treatment, wake after sleep onset (WASO) rate was reduced from 14.99 to 10.60 (IRR = 0.71; p = .006). Time and treatment were not found to be statistically significantly associated with any of the secondary outcomes. CONCLUSION: Yoga-based physical therapy is feasible and safe in the inpatient rehabilitation setting following TBI. Sleep quality improved following the addition of a one-hour seated rest in a relaxing environment to a standard rehabilitation daily schedule, suggesting that structured rest time may be beneficial to sleep hygiene during inpatient rehabilitation following TBI. ClinicalTrials.Gov Registration Number: NCT03701594.

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.

Users with spinal cord injury experience of robotic Locomotor exoskeletons: a qualitative study of the benefits, limitations, and recommendations.

BACKGROUND: Persons with spinal cord injury (SCI) may experience both psychological and physiological benefits from robotic locomotor exoskeleton use, and knowledgeable users may have valuable perspectives to inform future development. The objective of this study is to gain insight into the experiences, perspectives, concerns, and suggestions on the use of robotic locomotor exoskeletons by civilians and veterans living with SCI. METHODS: Participants reported their demographic characteristics and the extent of robotic exoskeleton use in an online survey. Then, 28 experienced robotic locomotor exoskeleton users participated in focus groups held at three regional hospitals that specialize in rehabilitation for persons with SCI. We used a qualitative description approach analysis to analyze the data, and included thematic analysis. RESULTS: Participants expressed that robotic exoskeletons were useful in therapy settings but, in their current form, were not practical for activities of daily living due to device limitations. Participants detailed the psychological benefits of being eye-level with their non-disabled peers and family members, and some reported physiologic improvements in areas such as bowel and bladder function. Participants detailed barriers of increased fatigue, spasticity, and spasms and expressed dissatisfaction with the devices due to an inability to use them independently and safely. Participants provided suggestions to manufacturers for technology improvements. CONCLUSIONS: The varied opinions and insights of robotic locomotor exoskeletons users with SCI add to our knowledge of device benefits and limitations.

Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults.

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20-70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.

Experience of Robotic Exoskeleton Use at Four Spinal Cord Injury Model Systems Centers.

BACKGROUND AND PURPOSE: Refinement of robotic exoskeletons for overground walking is progressing rapidly. We describe clinicians' experiences, evaluations, and training strategies using robotic exoskeletons in spinal cord injury rehabilitation and wellness settings and describe clinicians' perceptions of exoskeleton benefits and risks and developments that would enhance utility. METHODS: We convened focus groups at 4 spinal cord injury model system centers. A court reporter took verbatim notes and provided a transcript. Research staff used a thematic coding approach to summarize discussions. RESULTS: Thirty clinicians participated in focus groups. They reported using exoskeletons primarily in outpatient and wellness settings; 1 center used exoskeletons during inpatient rehabilitation. A typical episode of outpatient exoskeleton therapy comprises 20 to 30 sessions and at least 2 staff members are involved in each session. Treatment focuses on standing, stepping, and gait training; therapists measure progress with standardized assessments. Beyond improved gait, participants attributed physiological, psychological, and social benefits to exoskeleton use. Potential risks included falls, skin irritation, and disappointed expectations. Participants identified enhancements that would be of value including greater durability and adjustability, lighter weight, 1-hand controls, ability to navigate stairs and uneven surfaces, and ability to balance without upper extremity support. DISCUSSION AND CONCLUSIONS: Each spinal cord injury model system center had shared and distinct practices in terms of how it integrates robotic exoskeletons into physical therapy services. There is currently little evidence to guide integration of exoskeletons into rehabilitation therapy services and a pressing need to generate evidence to guide practice and to inform patients' expectations as more devices enter the market.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A231).

Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.

BACKGROUND: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions. OBJECTIVE: In this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting). METHODS: Thirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone's accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities. RESULTS: A classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%). CONCLUSIONS: Stroke-based training data is needed for high quality AR among gait-impaired individuals with stroke. Additionally, AR systems for home and community monitoring would likely benefit from including at-home activities in the training data.

Locomotor training improves reciprocal and nonreciprocal inhibitory control of soleus motoneurons in human spinal cord injury.

Pathologic reorganization of spinal networks and activity-dependent plasticity are common neuronal adaptations after spinal cord injury (SCI) in humans. In this work, we examined changes of reciprocal Ia and nonreciprocal Ib inhibition after locomotor training in 16 people with chronic SCI. The soleus H-reflex depression following common peroneal nerve (CPN) and medial gastrocnemius (MG) nerve stimulation at short conditioning-test (C-T) intervals was assessed before and after training in the seated position and during stepping. The conditioned H reflexes were normalized to the unconditioned H reflex recorded during seated. During stepping, both H reflexes were normalized to the maximal M wave evoked at each bin of the step cycle. In the seated position, locomotor training replaced reciprocal facilitation with reciprocal inhibition in all subjects, and Ib facilitation was replaced by Ib inhibition in 13 out of 14 subjects. During stepping, reciprocal inhibition was decreased at early stance and increased at midswing in American Spinal Injury Association Impairment Scale C (AIS C) and was decreased at midstance and midswing phases in AIS D after training. Ib inhibition was decreased at early swing and increased at late swing in AIS C and was decreased at early stance phase in AIS D after training. The results of this study support that locomotor training alters postsynaptic actions of Ia and Ib inhibitory interneurons on soleus motoneurons at rest and during stepping and that such changes occur in cases with limited or absent supraspinal inputs.

Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial.

BACKGROUND: Robots offer an alternative, potentially advantageous method of providing repetitive, high-dosage, and high-intensity training to address the gait impairments caused by stroke. In this study, we compared the effects of the Stride Management Assist (SMA®) System, a new wearable robotic device developed by Honda R&D Corporation, Japan, with functional task specific training (FTST) on spatiotemporal gait parameters in stroke survivors. METHODS: A single blinded randomized control trial was performed to assess the effect of FTST and task-specific walking training with the SMA® device on spatiotemporal gait parameters. Participants (n=50) were randomly assigned to FTST or SMA. Subjects in both groups received training 3 times per week for 6-8 weeks for a maximum of 18 training sessions. The GAITRite® system was used to collect data on subjects' spatiotemporal gait characteristics before training (baseline), at mid-training, post-training, and at a 3-month follow-up. RESULTS: After training, significant improvements in gait parameters were observed in both training groups compared to baseline, including an increase in velocity and cadence, a decrease in swing time on the impaired side, a decrease in double support time, an increase in stride length on impaired and non-impaired sides, and an increase in step length on impaired and non-impaired sides. No significant differences were observed between training groups; except for SMA group, step length on the impaired side increased significantly during self-selected walking speed trials and spatial asymmetry decreased significantly during fast-velocity walking trials. CONCLUSIONS: SMA and FTST interventions provided similar, significant improvements in spatiotemporal gait parameters; however, the SMA group showed additional improvements across more parameters at various time points. These results indicate that the SMA® device could be a useful therapeutic tool to improve spatiotemporal parameters and contribute to improved functional mobility in stroke survivors. Further research is needed to determine the feasibility of using this device in a home setting vs a clinic setting, and whether such home use provides continued benefits. TRIAL REGISTRATION: This study is registered under the title "Development of walk assist device to improve community ambulation" and can be located in clinicaltrials.gov with the study identifier: NCT01994395 .

Locomotor training improves premotoneuronal control after chronic spinal cord injury.

Spinal inhibition is significantly reduced after spinal cord injury (SCI) in humans. In this work, we examined if locomotor training can improve spinal inhibition exerted at a presynaptic level. Sixteen people with chronic SCI received an average of 45 training sessions, 5 days/wk, 1 h/day. The soleus H-reflex depression in response to low-frequency stimulation, presynaptic inhibition of soleus Ia afferent terminals following stimulation of the common peroneal nerve, and bilateral EMG recovery patterns were assessed before and after locomotor training. The soleus H reflexes evoked at 1.0, 0.33, 0.20, 0.14, and 0.11 Hz were normalized to the H reflex evoked at 0.09 Hz. Conditioned H reflexes were normalized to the associated unconditioned H reflex evoked with subjects seated, while during stepping both H reflexes were normalized to the maximal M wave evoked after the test H reflex at each bin of the step cycle. Locomotor training potentiated homosynaptic depression in all participants regardless the type of the SCI. Presynaptic facilitation of soleus Ia afferents remained unaltered in motor complete SCI patients. In motor incomplete SCIs, locomotor training either reduced presynaptic facilitation or replaced presynaptic facilitation with presynaptic inhibition at rest. During stepping, presynaptic inhibition was modulated in a phase-dependent manner. Locomotor training changed the amplitude of locomotor EMG excitability, promoted intralimb and interlimb coordination, and altered cocontraction between knee and ankle antagonistic muscles differently in the more impaired leg compared with the less impaired leg. The results provide strong evidence that locomotor training improves premotoneuronal control after SCI in humans at rest and during walking.

Locomotor training alters the behavior of flexor reflexes during walking in human spinal cord injury.

In humans, a chronic spinal cord injury (SCI) impairs the excitability of pathways mediating early flexor reflexes and increases the excitability of late, long-lasting flexor reflexes. We hypothesized that in individuals with SCI, locomotor training will alter the behavior of these spinally mediated reflexes. Nine individuals who had either chronic clinically motor complete or incomplete SCI received an average of 44 locomotor training sessions. Flexor reflexes, elicited via sural nerve stimulation of the right or left leg, were recorded from the ipsilateral tibialis anterior (TA) muscle before and after body weight support (BWS)-assisted treadmill training. The modulation pattern of the ipsilateral TA responses following innocuous stimulation of the right foot was also recorded in 10 healthy subjects while they stepped at 25% BWS to investigate whether body unloading during walking affects the behavior of these responses. Healthy subjects did not receive treadmill training. We observed a phase-dependent modulation of early TA flexor reflexes in healthy subjects with reduced body weight during walking. The early TA flexor reflexes were increased at heel contact, progressively decreased during the stance phase, and then increased throughout the swing phase. In individuals with SCI, locomotor training induced the reappearance of early TA flexor reflexes and changed the amplitude of late TA flexor reflexes during walking. Both early and late TA flexor reflexes were modulated in a phase-dependent pattern after training. These new findings support the adaptive capability of the injured nervous system to return to a prelesion excitability and integration state.

Primary motor and premotor cortex in implicit sequence learning--evidence for competition between implicit and explicit human motor memory systems.

Implicit and explicit memory systems for motor skills compete with each other during and after motor practice. Primary motor cortex (M1) is known to be engaged during implicit motor learning, while dorsal premotor cortex (PMd) is critical for explicit learning. To elucidate the neural substrates underlying the interaction between implicit and explicit memory systems, adults underwent a randomized crossover experiment of anodal transcranial direct current stimulation (AtDCS) applied over M1, PMd or sham stimulation during implicit motor sequence (serial reaction time task, SRTT) practice. We hypothesized that M1-AtDCS during practice will enhance online performance and offline learning of the implicit motor sequence. In contrast, we also hypothesized that PMd-AtDCS will attenuate performance and retention of the implicit motor sequence. Implicit sequence performance was assessed at baseline, at the end of acquisition (EoA), and 24 h after practice (retention test, RET). M1-AtDCS during practice significantly improved practice performance and supported offline stabilization compared with Sham tDCS. Performance change from EoA to RET revealed that PMd-AtDCS during practice attenuated offline stabilization compared with M1-AtDCS and sham stimulation. The results support the role of M1 in implementing online performance gains and offline stabilization for implicit motor sequence learning. In contrast, enhancing the activity within explicit motor memory network nodes such as the PMd during practice may be detrimental to offline stabilization of the learned implicit motor sequence. These results support the notion of competition between implicit and explicit motor memory systems and identify underlying neural substrates that are engaged in this competition.