Validity and reliability study of a novel surface electromyography sensor using a well-consolidated electromyography system in individuals with cervical spinal cord injury

STUDY DESIGN

Non-interventional, cross-sectional pilot study.

OBJECTIVES

To establish the validity and reliability of the BioStamp nPoint biosensor (Medidata Solutions, New York, NY, USA [formerly MC10, Inc.]) for measuring electromyography in individuals with cervical spinal cord injury (SCI) by comparing the surface electromyography (sEMG) metrics with the Trigno wireless electromyography system (Delsys, Natick, MA, USA).

SETTING

Participants were recruited from the Shirley Ryan AbilityLab registry.

METHODS

Individuals aged 18-70 years with cervical SCI were evaluated with the two biosensors to capture activity on upper-extremity muscles during two study sessions conducted over 2 days (day 1-consent alone; day 2-two data collections in same session). Time and frequency metrics were captured, and signal-to-noise ratio was determined for each muscle group. Test-retest reliability was determined using Pearson's correlation. Validation of the BioStamp nPoint system was based on Bland-Altmann analysis.

RESULTS

Among the 11 participants, 30.8% had subacute cervical injury at C5-C6; 53.8% were injured within 1 year of the study. Results from the test-retest reliability assessment revealed that most Pearson's correlations between the two sensory measurements were strong (≥0.50). The Bland-Altman analysis found values of the signal-to-noise ratio, frequency, and peak amplitude were within the level of agreement. Signal-to-noise ratios ranged from 7.06 to 22.1.

CONCLUSIONS

In most instances, the performance of the BioStamp nPoint sensors was moderately to strongly correlated with that of the Trigno sensors in all muscle groups tested. The BioStamp nPoint system is a valid and reliable approach to assess sEMG measures in individuals with cervical SCI.

SPONSORSHIP

The present study was supported by AbbVie Inc.

Noninvasive spinal stimulation improves walking in chronic stroke survivors: a proof-of-concept case series

BACKGROUND

After stroke, restoring safe, independent, and efficient walking is a top rehabilitation priority. However, in nearly 70% of stroke survivors asymmetrical walking patterns and reduced walking speed persist. This case series study aims to investigate the effectiveness of transcutaneous spinal cord stimulation (tSCS) in enhancing walking ability of persons with chronic stroke.

METHODS

Eight participants with hemiparesis after a single, chronic stroke were enrolled. Each participant was assigned to either the Stim group (N = 4, gait training + tSCS) or Control group (N = 4, gait training alone). Each participant in the Stim group was matched to a participant in the Control group based on age, time since stroke, and self-selected gait speed. For the Stim group, tSCS was delivered during gait training via electrodes placed on the skin between the spinous processes of C5-C6, T11-T12, and L1-L2. Both groups received 24 sessions of gait training over 8 weeks with a physical therapist providing verbal cueing for improved gait symmetry. Gait speed (measured from 10 m walk test), endurance (measured from 6 min walk test), spatiotemporal gait symmetries (step length and swing time), as well as the neurophysiological outcomes (muscle synergy, resting motor thresholds via spinal motor evoked responses) were collected without tSCS at baseline, completion, and 3 month follow-up.

RESULTS

All four Stim participants sustained spatiotemporal symmetry improvements at the 3 month follow-up (step length: 17.7%, swing time: 10.1%) compared to the Control group (step length: 1.1%, swing time 3.6%). Additionally, 3 of 4 Stim participants showed increased number of muscle synergies and/or lowered resting motor thresholds compared to the Control group.

CONCLUSIONS

This study provides promising preliminary evidence that using tSCS as a therapeutic catalyst to gait training may increase the efficacy of gait rehabilitation in individuals with chronic stroke. Trial registration NCT03714282 (clinicaltrials.gov), registration date: 2018-10-18.

Circulating T cell specific extracellular vesicle profiles in cardiac allograft acute cellular rejection

There is a critical need for biomarkers of acute cellular rejection (ACR) in organ transplantation. We hypothesized that ACR leads to changes in donor-reactive T cell small extracellular vesicle (sEV) profiles in transplant recipient circulation that match the kinetics of alloreactive T cell activation. In rodent heart transplantation, circulating T cell sEV quantities (P < .0001) and their protein and mRNA cargoes showed time-specific expression of alloreactive and regulatory markers heralding early ACR in allogeneic transplant recipients but not in syngeneic transplant recipients. Next generation sequencing of their microRNA cargoes identified novel candidate biomarkers of ACR, which were validated by stem loop quantitative reverse transcription polymerase chain reaction (n = 10). Circulating T cell sEVs enriched from allogeneic transplant recipients mediated targeted cytotoxicity of donor cardiomyocytes by apoptosis assay (P < .0001). Translation of the concept and EV methodologies to clinical heart transplantation demonstrated similar upregulation of circulating T cell sEV profiles at time points of grade 2 ACR (n = 3 patients). Furthermore, T cell receptor sequencing of T cell sEV mRNA cargo demonstrated expression of T cell clones with intact complementarity determining region 3 signals. These data support the diagnostic potential of T cell sEVs as noninvasive biomarker of ACR and suggest their potential functional roles.

Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis

OBJECTIVE

To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities.

DESIGN

Cross-sectional study.

SETTING

General community setting (ie, institutional research laboratory and community free-living).

PARTICIPANTS

Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection.

RESULTS

All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps).

CONCLUSIONS

Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.

Efficacy and safety of using auditory-motor entrainment to improve walking after stroke: a multi-site randomized controlled trial of InTandemTM

Walking slowly after stroke reduces health and quality of life. This multi-site, prospective, interventional, 2-arm randomized controlled trial (NCT04121754) evaluated the safety and efficacy of an autonomous neurorehabilitation system (InTandemTM) designed to use auditory-motor entrainment to improve post-stroke walking. 87 individuals were randomized to 5-week walking interventions with InTandem or Active Control (i.e., walking without InTandem). The primary endpoints were change in walking speed, measured by the 10-meter walk test pre-vs-post each 5-week intervention, and safety, measured as the frequency of adverse events (AEs). Clinical responder rates were also compared. The trial met its primary endpoints. InTandem was associated with a 2x larger increase in speed (Δ: 0.14 ± 0.03 m/s versus Δ: 0.06 ± 0.02 m/s, F(1,49) = 6.58, p = 0.013), 3x more responders (40% versus 13%, χ2(1) ≥ 6.47, p = 0.01), and similar safety (both groups experienced the same number of AEs). The auditory-motor intervention autonomously delivered by InTandem is safe and effective in improving walking in the chronic phase of stroke.

Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors

OBJECTIVE

Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge.

METHODS

Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data.

RESULTS

For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models.

CONCLUSION

These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke.

IMPACT

Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

AI in Rehabilitation Medicine: Opportunities and Challenges

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.

Evaluation of a carepartner-integrated telehealth gait rehabilitation program for persons with stroke: study protocol for a feasibility study

BACKGROUND

Despite family carepartners of individuals post-stroke experiencing high levels of strain and reduced quality of life, stroke rehabilitation interventions rarely address carepartner well-being or offer training to support their engagement in therapeutic activities. Our group has developed creative intervention approaches to support families during stroke recovery, thereby improving physical and psychosocial outcomes for both carepartners and stroke survivors. The purpose of this study is to test the feasibility of an adapted, home-based intervention (Carepartner Collaborative Integrative Therapy for Gait-CARE-CITE-Gait) designed to facilitate positive carepartner involvement during home-based training targeting gait and mobility.

METHODS

This two-phased design will determine the feasibility of CARE-CITE-Gait, a novel intervention that leverages principles from our previous carepartner-focused upper extremity intervention. During the 4-week CARE-CITE-Gait intervention, carepartners review online video-based modules designed to illustrate strategies for an autonomy-supportive environment during functional mobility task practice, and the study team completes two 2-h home visits for dyad collaborative goal setting. In phase I, content validity, usability, and acceptability of the CARE-CITE-Gait modules will be evaluated by stroke rehabilitation content experts and carepartners. In phase II, feasibility (based on measures of recruitment, retention, intervention adherence, and safety) will be measured. Preliminary effects of the CARE-CITE-Gait will be gathered using a single-group, quasi-experimental design with repeated measures (two baseline visits 1 week apart, posttest, and 1-month follow-up) with 15 carepartner and stroke survivor dyads. Outcome data collectors will be blinded. Outcomes include psychosocial variables (family conflict surrounding stroke recovery, strain, autonomy support, and quality of life) collected from carepartners and measures of functional mobility, gait speed, stepping activity, and health-related quality of life collected from stroke survivors.

DISCUSSION

The findings of the feasibility testing and preliminary data on the effects of CARE-CITE-Gait will provide justification and information to guide a future definitive randomized clinical trial. The knowledge gained from this study will enhance our understanding of and aid the development of rehabilitation approaches that address both carepartner and stroke survivor needs during the stroke recovery process.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05257928. Registered 25 February 2022.

TRIAL STATUS

This trial was registered on ClinicalTrials.gov (NCT05257928) on March 25, 2022. Recruitment of participants was initiated on May 18, 2022.

A Research Protocol to Study the Priming Effects of Breathing Low Oxygen on Enhancing Training-Related Gains in Walking Function for Persons With Spinal Cord Injury: The BO2ST Trial

Brief episodes of low oxygen breathing (therapeutic acute intermittent hypoxia; tAIH) may serve as an effective plasticity-promoting primer to enhance the effects of transcutaneous spinal stimulation-enhanced walking therapy (WALKtSTIM) in persons with chronic (>1 year) spinal cord injury (SCI). Pre-clinical studies in rodents with SCI show that tAIH and WALKtSTIM therapies harness complementary mechanisms of plasticity to maximize walking recovery. Here, we present a multi-site clinical trial protocol designed to examine the influence of tAIH + WALKtSTIM on walking recovery in persons with chronic SCI. We hypothesize that daily (eight sessions, 2 weeks) tAIH + WALKtSTIM will elicit faster, more persistent improvements in walking recovery than either treatment alone. To test our hypothesis, we are conducting a placebo-controlled clinical trial on 60 SCI participants who randomly receive one of three interventions: tAIH + WALKtSTIM; Placebo + WALKtSTIM; and tAIH + WALKtSHAM. Participants receive daily tAIH (fifteen 90-sec episodes at 10% O2 with 60-sec intervals at 21% O2) or daily placebo (fifteen 90-sec episodes at 21% O2 with 60-sec intervals at 21% O2) before a 45-min session of WALKtSTIM or WALKtSHAM. Our primary outcome measures assess walking speed (10-Meter Walk Test), endurance (6-Minute Walk Test), and balance (Timed Up and Go Test). For safety, we also measure pain levels, spasticity, sleep behavior, cognition, and rates of systemic hypertension and autonomic dysreflexia. Assessments occur before, during, and after sessions, as well as at 1, 4, and 8 weeks post-intervention. Results from this study extend our understanding of the functional benefits of tAIH priming by investigating its capacity to boost the neuromodulatory effects of transcutaneous spinal stimulation on restoring walking after SCI. Given that there is no known cure for SCI and no single treatment is sufficient to overcome walking deficits, there is a critical need for combinatorial treatments that accelerate and anchor walking gains in persons with lifelong SCI.

Trial Registration

ClinicalTrials.gov, NCT05563103.

Functional Performance Outcomes of a Powered Knee-Ankle Prosthesis in Service Members With Unilateral Transfemoral Limb Loss

INTRODUCTION

Clinical knowledge surrounding functional outcomes of a powered knee-ankle (PKA) device is limited, particularly among younger and active populations with limb loss. Here, three service members (SM) with unilateral transfemoral limb loss received an optimally tuned PKA prosthesis and device-specific training.

MATERIALS AND METHODS

Once proficiency with the PKA device was demonstrated on benchmark activities, and outcomes with the PKA and standard-of-care (SoC) prostheses were obtained via a modified graded treadmill test, 6-minute walk test, and overground gait assessment.

RESULTS

All SM demonstrated proficiency with the PKA prosthesis within the minimum three training sessions. With the PKA versus SoC prosthesis, cost of transport during the modified graded treadmill test was 4.0% ± 5.2% lower at slower speeds (i.e., 0.6-1.2 m/s), but 7.0% ± 5.1% greater at the faster walking speeds (i.e., ≥1.4 m/s). For the 6-minute walk test, SM walked 83.9 ± 13.2 m shorter with the PKA versus SoC prosthesis. From the overground gait assessment, SM walked with 20.6% ± 10.5% greater trunk lateral flexion and 31.8% ± 12.8% greater trunk axial rotation ranges of motion, with the PKA versus SoC prosthesis.

CONCLUSIONS

Compared to prior work with the PKA in a civilian cohort, although SM demonstrated faster device proficiency (3 versus 12 sessions), SM walked with greater compensatory motions compared to their SoC prostheses (contrary to the civilian cohort). As such, it is important to understand patient-specific factors among various populations with limb loss for optimizing device-specific training and setting functional goals for occupational and/or community reintegration, as well as reducing the risk for secondary complications over the long term.

Activity Monitoring in Parkinson Disease: A Qualitative Study of Implementation Determinants

BACKGROUND AND PURPOSE

There is interest in incorporating digital health technology in routine practice. We integrate multiple stakeholder perspectives to describe implementation determinants (barriers and facilitators) regarding digital health technology use to facilitate exercise behavior change for people with Parkinson disease in outpatient physical therapy.

METHODS

The purposeful sample included people with Parkinson disease (n = 13), outpatient physical therapists (n = 12), and advanced technology stakeholders including researchers and reimbursement specialists (n = 13). Semistructured interviews were used to elicit implementation determinants related to using digital health technology for activity monitoring and exercise behavior change. Deductive codes based on the Consolidated Framework for Implementation Research were used to describe implementation determinants.

RESULTS

Key implementation determinants were similar across stakeholder groups. Essential characteristics of digital health technology included design quality and packaging, adaptability, complexity, and cost. Implementation of digital health technology by physical therapists and people with Parkinson disease was influenced by their knowledge, attitudes, and varied confidence levels in using digital health technology. Inner setting organizational determinants included available resources and access to knowledge/information. Process determinants included device interoperability with medical record systems and workflow integration. Outer setting barriers included lack of external policies, regulations, and collaboration with device companies.

DISCUSSION AND CONCLUSIONS

Future implementation interventions should address key determinants, including required processes for how and when physical therapists instruct people with Parkinson disease on digital health technology, organizational readiness, workflow integration, and characteristics of physical therapists and people with Parkinson disease who may have ingrained beliefs regarding their ability and willingness to use digital health technology. Although site-specific barriers should be addressed, digital health technology knowledge translation tools tailored to individuals with varied confidence levels may be generalizable across clinics.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content available at: http://links.lww.com/JNPT/A436 ).

Understanding corticomotor mechanisms for activation of non-target muscles during unilateral isometric contractions of leg muscles after stroke

PURPOSE

Muscle activation often occurs in muscles ipsilateral to a voluntarily activated muscle and to a greater extent after stroke. In this study, we measured muscle activation in non-target, ipsilateral leg muscles and used transcranial magnetic stimulation (TMS) to provide insight into whether corticomotor pathways contribute to involuntary activation.

MATERIALS AND METHODS

Individuals with stroke performed unilateral isometric ankle dorsiflexion, ankle plantarflexion, knee extension, and knee flexion. To quantify involuntary muscle activation in non-target muscles, muscle activation was measured during contractions from the ipsilateral tibialis anterior (TA), medial gastrocnemius (MG), rectus femoris (RF), and biceps femoris (BF) and normalized to resting muscle activity. To provide insight into mechanisms of involuntary non-target muscle activation, TMS was applied to the contralateral hemisphere, and motor evoked potentials (MEPs) were recorded.

RESULTS

We found significant muscle activation in nearly every non-target muscle during isometric unilateral contractions. MEPs were frequently observed in non-target muscles, but greater non-target MEP amplitude was not associated with greater non-target muscle activation.

CONCLUSIONS

Our results suggest that non-target muscle activation occurs frequently in individuals with chronic stroke. The lack of association between non-target TMS responses and non-target muscle activation suggests that non-target muscle activation may have a subcortical or spinal origin. Non-target muscle activation has important clinical implications because it may impair torque production, out-of-synergy movement, and muscle activation timing.

Protocol for a randomized controlled trial to evaluate a year-long (NICU-to-home) evidence-based, high dose physical therapy intervention in infants at risk of neuromotor delay

INTRODUCTION

Developmental disabilities and neuromotor delay adversely affect long-term neuromuscular function and quality of life. Current evidence suggests that early therapeutic intervention reduces the severity of motor delay by harnessing neuroplastic potential during infancy. To date, most early therapeutic intervention trials are of limited duration and do not begin soon after birth and thus do not take full advantage of early neuroplasticity. The Corbett Ryan-Northwestern-Shirley Ryan AbilityLab-Lurie Children's Infant Early Detection, Intervention and Prevention Project (Project Corbett Ryan) is a multi-site longitudinal randomized controlled trial to evaluate the efficacy of an evidence-based physical therapy intervention initiated in the neonatal intensive care unit (NICU) and continuing to 12 months of age (corrected when applicable). The study integrates five key principles: active learning, environmental enrichment, caregiver engagement, a strengths-based approach, and high dosage (ClinicalTrials.gov identifier NCT05568264).

METHODS

We will recruit 192 infants at risk for neuromotor delay who were admitted to the NICU. Infants will be randomized to either a standard-of-care group or an intervention group; infants in both groups will have access to standard-of-care services. The intervention is initiated in the NICU and continues in the infant's home until 12 months of age. Participants will receive twice-weekly physical therapy sessions and caregiver-guided daily activities, assigned by the therapist, targeting collaboratively identified goals. We will use various standardized clinical assessments (General Movement Assessment; Bayley Scales of Infant and Toddler Development, 4th Edition (Bayley-4); Test of Infant Motor Performance; Pediatric Quality of Life Inventory Family Impact Module; Alberta Infant Motor Scale; Neurological, Sensory, Motor, Developmental Assessment; Hammersmith Infant Neurological Examination) as well as novel technology-based tools (wearable sensors, video-based pose estimation) to evaluate neuromotor status and development throughout the course of the study. The primary outcome is the Bayley-4 motor score at 12 months; we will compare scores in infants receiving the intervention vs. standard-of-care therapy.

Validity and reliability of a commercial wearable sensor system for measuring spatiotemporal gait parameters in a post-stroke population: the effects of walking speed and asymmetry

Objective.Commercial wearable sensor systems are a promising alternative to costly laboratory equipment for clinical gait evaluation, but their accuracy for individuals with gait impairments is not well established. Therefore, we investigated the validity and reliability of the APDM Opal wearable sensor system to measure spatiotemporal gait parameters for healthy controls and individuals with chronic stroke.Approach.Participants completed the 10 m walk test over an instrumented mat three times in different speed conditions. We compared performance of Opal sensors to the mat across different walking speeds and levels of step length asymmetry in the two populations.Main results. Gait speed and stride length measures achieved excellent reliability, though they were systematically underestimated by 0.11 m s-1and 0.12 m, respectively. The stride and step time measures also achieved excellent reliability, with no significant errors (median absolute percentage error <6.00%,p> 0.05). Gait phase duration measures achieved moderate-to-excellent reliability, with relative errors ranging from 4.13%-21.59%. Across gait parameters, the relative error decreased by 0.57%-9.66% when walking faster than 1.30 m s-1; similar reductions occurred for step length symmetry indices lower than 0.10.Significance. This study supports the general use of Opal wearable sensors to obtain quantitative measures of post-stroke gait impairment. These measures should be interpreted cautiously for individuals with moderate-severe asymmetry or walking speeds slower than 0.80 m s-1.

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

Quantum bath suppression in a superconducting circuit by immersion cooling

Quantum circuits interact with the environment via several temperature-dependent degrees of freedom. Multiple experiments to-date have shown that most properties of superconducting devices appear to plateau out at T ≈ 50 mK - far above the refrigerator base temperature. This is for example reflected in the thermal state population of qubits, in excess numbers of quasiparticles, and polarisation of surface spins - factors contributing to reduced coherence. We demonstrate how to remove this thermal constraint by operating a circuit immersed in liquid ³He. This allows to efficiently cool the decohering environment of a superconducting resonator, and we see a continuous change in measured physical quantities down to previously unexplored sub-mK temperatures. The ³He acts as a heat sink which increases the energy relaxation rate of the quantum bath coupled to the circuit a thousand times, yet the suppressed bath does not introduce additional circuit losses or noise. Such quantum bath suppression can reduce decoherence in quantum circuits and opens a route for both thermal and coherence management in quantum processors.

Evaluation of a Carepartner-Integrated Telehealth Gait Rehabilitation Program for Persons with Stroke : Study Protocol for a Feasibility Study

Background

Despite family carepartners of individuals post-stroke experiencing high levels of strain and reduced quality of life, stroke rehabilitation interventions rarely address carepartner well-being or offer training to support their engagement in therapeutic activities. Our group has developed creative intervention approaches to support families during stroke recovery, thereby improving physical and psychosocial outcomes for both carepartners and stroke survivors. The purpose of this preliminary clinical trial is to test the feasibility of an adapted, home-based intervention (Carepartner Collaborative Integrative Therapy for Gait-CARE-CITE-Gait) designed to facilitate positive carepartner involvement during home-based training targeting gait and mobility.

Methods

This two-phased study will determine the feasibility of CARE-CITE-Gait, a novel intervention developed by our team that leverages principles from our previous carepartner-focused upper extremity intervention. During the 4-week CARE-CITE-Gait intervention, carepartners review online video-based modules designed to illustrate strategies for an autonomy-supportive environment during functional mobility task practice, and the study team completes two 2-hour (home-based) visits for dyad collaborative goal setting. In Phase I, the usability and acceptability of the CARE-CITE-Gait modules will be evaluated by stroke rehabilitation content experts and carepartners. In Phase II, feasibility (based on measures of recruitment, retention, and intervention adherence) will be measured. Preliminary effects of the CARE-CITE-Gait will be gathered using a single-group, evaluator blinded, quasi-experimental design with repeated measures (two baseline visits one week apart, post-test, and one-month follow-up) with 15 carepartner and stroke survivor dyads. Outcomes include psychosocial variables (strain, family conflict surrounding stroke recovery, autonomy support and life changes) collected from carepartners, and measures of functional mobility, gait speed, stepping activity, and health-related quality of life collected from stroke survivors.

Discussion

The findings of the feasibility testing and preliminary data on the effects of CARE-CITE-Gait will provide justification and information to guide a future definitive randomized clinical trial. The knowledge gained from this study will enhance our understanding of and aid the development of rehabilitation approaches that address both carepartner and stroke survivor needs during the stroke recovery process.

Trial Registration

ClinicalTrials.gov, NCT05257928. Registered 25 February 2022, https://clinicaltrials.gov/ct2/show/NCT05257928.

Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.

Abstract WMP27: Augmenting Early Prediction Of Post-Rehabilitation Ambulation Ability And Risk Of Falling Status For Recovering Stroke Patients Using Wearable Sensors And Machine Learning

Introduction: Inertial measurement units (IMU) can capture objective biomarkers of recovery during everyday activity paving the way to early prognosis and personalized therapy in post-stroke rehabilitation. In this study, we evaluated the performance of IMU data incorporated into machine learning models to predict discharge abilities compared to models based on traditional clinical variables.

Methods: 32 subacute stroke patients admitted to inpatient rehabilitation performed clinical tests while wearing IMU sensors placed on the pelvis and both unaffected and affected-side shanks (US, AS). Variables collected were patient information (PI), clinician-scored functional assessments (FA), signal-processed IMU data during the 10-Meter Walk Test (IMU 10MWT ) and the Berg-Balance Scale (IMU BBS ). Patients were identified as household or community ambulators (Amb), and with high or normal risk of fall (RoF) based on discharge functional scores. A class-weighted L1-penalized logistic regression model was trained using nested leave-one-out cross-validation on admission data to predict discharge Amb and RoF statuses. The impact of sensor data on model performance was assessed on three feature sets: PI+FA, PI+IMU 10MWT/BBS , PI+FA+IMU 10MWT/BBS . Evaluation metrics included: weighted F1 score (WF1), accuracy, log-loss, and feature importance.

Results: The PI+FA+IMU BBS , and both the PI+IMU 10MWT and PI+FA+IMU 10MWT models, outperformed the PI + FA benchmark model for RoF and Amb prediction, respectively, across all metrics (Figure 1). US gyroscopic and AS/US acceleration were relevant for Amb prediction, while the admission BBS score and pelvic acceleration were important to RoF prediction.

Conclusions: Incorporation of simple IMU data from admission can improve discharge functional predictions over a model using PI and FA. Early, accurate predictions of post-stroke recovery could facilitate more personalized and efficient rehabilitation strategies.

Cost-effectiveness analysis of overground robotic training versus conventional locomotor training in people with spinal cord injury

BACKGROUND

Few, if any estimates of cost-effectiveness for locomotor training strategies following spinal cord injury (SCI) are available. The purpose of this study was to estimate the cost-effectiveness of locomotor training strategies following spinal cord injury (overground robotic locomotor training versus conventional locomotor training) by injury status (complete versus incomplete) using a practice-based cohort.

METHODS

A probabilistic cost-effectiveness analysis was conducted using a prospective, practice-based cohort from four participating Spinal Cord Injury Model System sites. Conventional locomotor training strategies (conventional training) were compared to overground robotic locomotor training (overground robotic training). Conventional locomotor training included treadmill-based training with body weight support, overground training, and stationary robotic systems. The outcome measures included the calculation of quality adjusted life years (QALYs) using the EQ-5D and therapy costs. We estimate cost-effectiveness using the incremental cost utility ratio and present results on the cost-effectiveness plane and on cost-effectiveness acceptability curves.

RESULTS

Participants in the prospective, practice-based cohort with complete EQ-5D data (n = 99) qualified for the analysis. Both conventional training and overground robotic training experienced an improvement in QALYs. Only people with incomplete SCI improved with conventional locomotor training, 0.045 (SD 0.28), and only people with complete SCI improved with overground robotic training, 0.097 (SD 0.20). Costs were lower for conventional training, $1758 (SD $1697) versus overground robotic training $3952 (SD $3989), and lower for those with incomplete versus complete injury. Conventional overground training was more effective and cost less than robotic therapy for people with incomplete SCI. Overground robotic training was more effective and cost more than conventional training for people with complete SCI. The incremental cost utility ratio for overground robotic training for people with complete spinal cord injury was $12,353/QALY.

CONCLUSIONS

The most cost-effective locomotor training strategy for people with SCI differed based on injury completeness. Conventional training was more cost-effective than overground robotic training for people with incomplete SCI. Overground robotic training was more cost-effective than conventional training for people with complete SCI. The effect estimates may be subject to limitations associated with small sample sizes and practice-based evidence methodology. These estimates provide a baseline for future research.

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).

A framework for clinical utilization of robotic exoskeletons in rehabilitation

Exoskeletons are externally worn motorized devices that assist with sit-to-stand and walking in individuals with motor and functional impairments. The Food & Drug Administration (FDA) has approved several of these technologies for clinical use however, there is limited evidence to guide optimal utilization in every day clinical practice. With the diversity of technologies & equipment available, it presents a challenge for clinicians to decide which device to use, when to initiate, how to implement these technologies with different patient presentations, and when to wean off the devices. Thus, we present a clinical utilization framework specific to exoskeletons with four aims.

These aims are to assist with clinical decision making of when exoskeleton use is clinically indicated, identification of which device is most appropriate based on patient deficits and device characteristics, providing guidance on dosage parameters within a plan of care and guidance for reflection following utilization. This framework streamlines how clinicians can approach implementation through the synthesis of published evidence with appropriate clinical assessment & device selection to reflection for success and understanding of these innovative & complex technologies.

Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation

We created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single metric using a seven-layer deep autoencoder, with the developed metric compared to similar results from principal component analysis (PCA). The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study. This single summary metric permitted a cross-validated reconstruction of all eight scores, accounting for 83.29% of the variance. The derived score is also linked to the overall functional ability in this limited trial population, as improvements in each base clinical score led to increases in this developed metric. There was a highly significant increase in this autoencoder-based metric when the subjects used the microprocessor-controlled knee (p < 0.001, repeated measures ANOVA). A traditional PCA metric led to a similar interpretation but captured only 67.3% of the variance. The autoencoder composite score represents a single-valued, succinct summary that can be useful for the holistic assessment of highly variable, individual scores in limited clinical datasets.

Muscle adaptations in acute SCI following overground exoskeleton + FES training: A pilot study

Objective

To evaluate the combined effects of robotic exoskeleton and functional electrical stimulation (FES) training on muscle composition during over-ground gait training in persons with acute spinal cord injury (SCI).

Design

Randomized crossover pilot study.

Setting

Inpatient-rehabilitation Hospital.

Participants

Six individuals with acute SCI.

Intervention

Participants were randomized to either receive training with the Ekso® Bionics exoskeleton combined with FES in addition to standard-of-care or standard-of-care alone.

Outcome measures

The main outcome measures for the study were quantified using magnetic resonance imaging (MRI), specifically, lower extremity muscle volume and intramuscular adipose tissue (IMAT). Static balance and fall risk were assessed using the Berg Balance Scale.

Results

Significant improvements were observed in muscle volume in the exoskeleton intervention group when compared to only standard-of-care (p < 0.001). There was no significant difference between the groups in IMAT even though the intervention group saw a reduction in IMAT that trended towards statistical significance (p = 0.07). Static balance improved in both groups, with greater improvements seen in the intervention group.

Conclusions

Early intervention with robotic exoskeleton may contribute to improved muscle function measured using MRI in individuals with acute SCI.

Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation

OBJECTIVE

A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients reintegrate safely in the home and community. However, not all patients have the same potential for recovery, making it difficult to set realistic therapy goals and to anticipate future needs for short- or long-term care. The objective of this study was to test the value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation.

METHOD

Supervised machine learning algorithms were trained to classify patients as either household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments.

RESULTS

Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%.

CONCLUSIONS

This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors.

CLINICAL IMPACT

Accurately predicting a patient's functional recovery early in the rehabilitation process would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment.

Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors

Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.

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.

Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study

Background

Stroke is a leading cause of serious gait impairments and restoring walking ability is a major goal of physical therapy interventions. Soft robotic exosuits are portable, lightweight, and unobtrusive assistive devices designed to improve the mobility of post-stroke individuals through facilitation of more natural paretic limb function during walking training. However, it is unknown whether long-term gait training using soft robotic exosuits will clinically impact gait function and quality of movement post-stroke.

Objective

The objective of this pilot study was to examine the therapeutic effects of soft robotic exosuit-augmented gait training on clinical and biomechanical gait outcomes in chronic post-stroke individuals.

Methods

Five post-stroke individuals received high intensity gait training augmented with a soft robotic exosuit, delivered in 18 sessions over 6–8 weeks. Performance based clinical outcomes and biomechanical gait quality parameters were measured at baseline, midpoint, and completion.

Results

Clinically meaningful improvements were observed in walking speed (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p < 0.05) and endurance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p < 0.01) together with other traditional gait related outcomes. The gait quality measures including hip (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p < 0.01) and knee (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p < 0.05) flexion/extension exhibited an increase in range of motion in a symmetric manner (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p < 0.05). We also observed an increase in bilateral ankle angular velocities (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document}p < 0.05), suggesting biomechanical improvements in walking function.

Conclusions

The results in this study offer preliminary evidence that a soft robotic exosuit can be a useful tool to augment high intensity gait training in a clinical setting. This study justifies more expanded research on soft exosuit technology with a larger post-stroke population for more reliable generalization.

Trial registration This study is registered with ClinicalTrials.gov (ID: NCT04251091)

Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study

Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson's correlation with the reference system was moderate for swing times (r = 0.4-0.66), but stronger for stance and double support time (r = 0.93-0.95). Cadence mean error was -0.25 steps/min ± 3.9 steps/min (r = 0.97), while walking speed mean error was -0.02 ± 0.11 m/s (r = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment.

Walking improvement in chronic incomplete spinal cord injury with exoskeleton robotic training (WISE): a randomized controlled trial

STUDY DESIGN

Clinical trial.

OBJECTIVE

To demonstrate that a 12-week exoskeleton-based robotic gait training regimen can lead to a clinically meaningful improvement in independent gait speed, in community-dwelling participants with chronic incomplete spinal cord injury (iSCI).

SETTING

Outpatient rehabilitation or research institute.

METHODS

Multi-site (United States), randomized, controlled trial, comparing exoskeleton gait training (12 weeks, 36 sessions) with standard gait training or no gait training (2:2:1 randomization) in chronic iSCI (>1 year post injury, AIS-C, and D), with residual stepping ability. The primary outcome measure was change in robot-independent gait speed (10-meter walk test, 10MWT) post 12-week intervention. Secondary outcomes included: Timed-Up-and-Go (TUG), 6-min walk test (6MWT), Walking Index for Spinal Cord Injury (WISCI-II) (assistance and devices), and treating therapist NASA-Task Load Index.

RESULTS

Twenty-five participants completed the assessments and training as assigned (9 Ekso, 10 Active Control, 6 Passive Control). Mean change in gait speed at the primary endpoint was not statistically significant. The proportion of participants with improvement in clinical ambulation category from home to community speed post-intervention was greatest in the Ekso group (>1/2 Ekso, 1/3 Active Control, 0 Passive Control, p < 0.05). Improvements in secondary outcome measures were not significant.

CONCLUSIONS

Twelve weeks of exoskeleton robotic training in chronic SCI participants with independent stepping ability at baseline can improve clinical ambulatory status. Improvements in raw gait speed were not statistically significant at the group level, which may guide future trials for participant inclusion criteria. While generally safe and tolerable, larger gains in ambulation might be associated with higher risk for non-serious adverse events.

Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

Introduction

Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning.

Methods

Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls).

Results

These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037).

Discussion

Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

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.

Utilization of Robotic Exoskeleton for Overground Walking in Acute and Chronic Stroke

Stroke commonly results in gait deficits which impacts functional ambulation and quality of life. Robotic exoskeletons (RE) for overground walking are devices that are programmable to provide high dose and movement-impairment specific assistance thus offering new rehabilitation possibilities for recovery progression in individuals post stroke. The purpose of this investigation is to present preliminary utilization data in individuals with acute and chronic stroke after walking overground with an RE. Secondary analysis on a subset of individuals is presented to understand the mechanistic changes due to RE overground walking. Thirty-eight participants with hemiplegia secondary to stroke were enrolled in a clinical trial conducted at eight rehabilitation centers. Data is presented for four sessions of overground walking in the RE over the course of 2 weeks. Participants continued their standard of care if they had any ongoing therapy at the time of study enrollment. Gait speed during the 10 Meter Walk Test, Gait deviations and the Functional Ambulation Category (FAC) data were collected before (baseline) and after (follow-up) the RE walking sessions. Walking speed significantly increased between baseline and follow-up for participants in the chronic (p <0.01) and acute (p < 0.05) stage of stroke recovery. FAC level significantly improved (p < 0.05) and there were significantly fewer (p < 0.05) gait deviations observed for participants in the acute stages of stroke recovery between baseline and follow-up. Secondary analysis on a subset of eight participants indicated that after four sessions of overground walking with the RE, the participants significantly improved their spatial symmetry. The walk time, step count and ratio of walk time to up time increased from first session to the last session for participants in the chronic and acute stages of stroke. The RE was effectively utilized for overground walking for individuals with acute and chronic stroke with varying severity levels. The results demonstrated an increase in walking speed, improvement in FAC and a decrease in gait deviations (from baseline to follow-up) after four sessions of overground walking in the RE for participants. In addition, preliminary data indicated that spatial symmetry and step length also improved after utilization of an RE for overground walking.

Usability, functionality, and efficacy of a custom myoelectric elbow-wrist-hand orthosis to assist elbow function in individuals with stroke

Introduction

After stroke, upper limb impairment affects independent performance of activities of daily living. We evaluated the usability, functionality, and efficacy of a myoelectric elbow-wrist-hand orthosis to provide support, limit unsafe motion, and enhance the functional motion of paralyzed or weak upper limbs.

Methods

Individuals with stroke participated in a single-session study to evaluate the device. Ability to activate the device was tested in supported and unsupported shoulder position, as well as the elbow range of motion, ability to maintain elbow position, and ability to lift and hold a range of weights while using the device.

Results

No adverse events were reported. 71% of users were able to operate the device in all three active myoelectric activation modes (Biceps, Triceps, Dual) during testing. Users were able to hold a range of wrist weights (0.5–2 lbs) for 10–120 seconds, with the largest percentage of participants able to hold weights with the device in Biceps Mode.

Conclusions

The myoelectric elbow-wrist-hand orthosis improved range of motion during use and was efficacious at remediating upper extremity impairment after stroke. All users could operate the device in at least one mode, and most could lift and hold weights representative of some everyday objects using the device.

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.

Combining Accelerometer and GPS Features to Evaluate Community Mobility in Knee Ankle Foot Orthoses (KAFO) Users

Orthotic and assistive devices such as knee ankle foot orthoses (KAFO), come in a variety of forms and fits, with several levels of available features that could help users perform daily activities more naturally. However, objective data on the actual use of these devices outside of the research lab is usually not obtained. Such data could enhance traditional lab-based outcome measures and inform clinical decision-making when prescribing new orthotic and assistive technology. Here, we link data from a GPS unit and an accelerometer mounted on the orthotic device to quantify its usage in the community and examine the correlations with clinical metrics. We collected data from 14 individuals over a period of 2 months as they used their personal KAFO first, and then a novel research KAFO; for each device we quantified number of steps, cadence, time spent at community locations and time wearing the KAFO at those locations. Sensor-derived metrics showed that mobility patterns differed widely between participants (mean steps: 591.3, SD =704.2). The novel KAFO generally enabled participants to walk faster during clinical tests ( ∆6 Minute-Walk-Test=71.5m, p=0.006). However, some participants wore the novel device less often despite improved performance on these clinical measures, leading to poor correlation between changes in clinical outcome measures and changes in community mobility ( ∆6 Minute-Walk-Test - ∆ Community Steps: r=0.09, p=0.76). Our results suggest that some traditional clinical outcome measures may not be associated with the actual wear time of an assistive device in the community, and obtaining personalized data from real-world use through wearable technology is valuable.

A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking With Transcutaneous Spinal Cord Stimulation: A Pilot Study

Transcutaneous spinal cord electrical stimulation (tSCS) is an emerging technology that targets to restore functionally integrated neuromuscular control of gait. The purpose of this study was to demonstrate a novel filtering method, Artifact Component Specific Rejection (ACSR), for removing artifacts induced by tSCS from surface electromyogram (sEMG) data for investigation of muscle response during walking when applying spinal stimulation. Both simulated and real tSCS contaminated sEMG data from six stroke survivors were processed using ACSR and notch filtering, respectively. The performance of the filters was evaluated with data collected in various conditions (e.g., simulated artifacts contaminating sEMG in multiple degrees, various tSCS intensities in five lower-limb muscles of six participants). In the simulation test, after applying the ACSR filter, the contaminated-signal was well matched with the original signal, showing a high correlation (r = 0.959) and low amplitude difference (normalized root means square error = 0.266) between them. In the real tSCS contaminated data, the ACSR filter showed superior performance on reducing the artifacts (96% decrease) over the notch filter (25% decrease). These results indicate that ACSR filtering is capable of eliminating artifacts from sEMG collected during tSCS application, improving the precision of quantitative analysis of muscle activity.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00879-3.

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.

Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients

Soft, skin-integrated electronic sensors can provide continuous measurements of diverse physiological parameters, with broad relevance to the future of human health care. Motion artifacts can, however, corrupt the recorded signals, particularly those associated with mechanical signatures of cardiopulmonary processes. Design strategies introduced here address this limitation through differential operation of a matched, time-synchronized pair of high-bandwidth accelerometers located on parts of the anatomy that exhibit strong spatial gradients in motion characteristics. When mounted at a location that spans the suprasternal notch and the sternal manubrium, these dual-sensing devices allow measurements of heart rate and sounds, respiratory activities, body temperature, body orientation, and activity level, along with swallowing, coughing, talking, and related processes, without sensitivity to ambient conditions during routine daily activities, vigorous exercises, intense manual labor, and even swimming. Deployments on patients with COVID-19 allow clinical-grade ambulatory monitoring of the key symptoms of the disease even during rehabilitation protocols.

Evaluation of a lower-extremity robotic exoskeleton for people with knee osteoarthritis

A multi-site study was conducted to evaluate the efficacy of the Keeogo™ exoskeleton as a mobility assist device for use in the clinic and at home in people with knee osteoarthritis (KOA). Twenty-four participants were randomized in a two-stage cross-over design that evaluated the immediate effects of using the exoskeleton in the clinic and the cumulative effects of training and home use. Immediate effects were quantified by comparing 1) physical performance with|without (W|WO) the device during a battery of mobility tests, and 2) physical activity levels at home (actigraphy) for one month, two weeks W|WO the device. Cumulative effects were quantified as change in physical performance W and WO over time. WOMAC and other self-report scales were measured and usability assessed. There were no immediate effects on physical performance or physical activity at home; however, there were cumulative effects as indicated by improved stair time (p = .001) as well as improved WOMAC pain (p = .004) and function (p = .003). There was a direct relationship between improved physical function and improved WOMAC pain (r = -.677, p < .001) and stiffness (r = .537, p = .007). Weight and battery life were identified as important to usability. A full-scale RCT with more participants, longer study period, and better usage monitoring is warranted.

Relationship between gait quality measures and modular neuromuscular control parameters in chronic post-stroke individuals

Background

Recent evidence suggests that disinhibition and/or hyperexcitation of the brainstem descending pathways and intraspinal motor network diffuse spastic synergistic activation patterns after stroke. This results in simplified or merged muscle sets (i.e., muscle modules or synergies) compared to non-impaired individuals and this leads to poor walking performance. However, the relations of how these neuromuscular deficits influence gait quality (e.g., symmetry or natural walking patterns) are still unclear. The objective of this exploratory study was to investigate the relations of modular neuromuscular framework and gait quality measures in chronic stroke individuals.

Methods

Sixteen chronic post-stroke individuals participated in this study. Full lower body three-dimensional kinematics and electromyography (EMG) were concurrently measured during overground walking at a comfortable speed. We first examined changes in gait quality measures across the number of muscle modules using linear regression model. Then, a stepwise multiple regression was used to investigate the optimal combination of the neuromuscular parameters that associates with gait quality measures.

Results

We observed that subjects who had a lower number of muscle modules revealed reduced function (i.e., speed) and greater asymmetry in the kinematic parameters including limb length, footpath area, knee flexion/extension, and hip abduction/adduction (all p < 0.05). We also found that the combination of input variables from the modular neuromuscular control framework significantly associated with gait quality measures (average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}=42.5\mathrm{\%}$$\end{document}R2=42.5%). Those variables included variability accounted for (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$VAF$$\end{document}VAF) information from the muscle modules and area under the EMG envelope curves of the quadriceps (i.e., rectus femoris and vastus lateralis) and tibialis anterior muscles.

Conclusions

The results suggest that there exists a significant correlation between the neuromuscular control framework and the gait quality measures. This study helps to understand the underlying mechanism of disturbances in gait quality and provides insight for a more comprehensive outcome measure to assess gait impairment after stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00860-0.

Rapid Screening of Physiological Changes Associated With COVID-19 Using Soft-Wearables and Structured Activities: A Pilot Study

OBJECTIVE

Controlling the spread of the COVID-19 pandemic largely depends on scaling up the testing infrastructure for identifying infected individuals. Consumer-grade wearables may present a solution to detect the presence of infections in the population, but the current paradigm requires collecting physiological data continuously and for long periods of time on each individual, which poses limitations in the context of rapid screening. Technology: Here, we propose a novel paradigm based on recording the physiological responses elicited by a short (~2 minutes) sequence of activities (i.e. "snapshot"), to detect symptoms associated with COVID-19. We employed a novel body-conforming soft wearable sensor placed on the suprasternal notch to capture data on physical activity, cardio-respiratory function, and cough sounds.

RESULTS

We performed a pilot study in a cohort of individuals (n=14) who tested positive for COVID-19 and detected altered heart rate, respiration rate and heart rate variability, relative to a group of healthy individuals (n=14) with no known exposure. Logistic regression classifiers were trained on individual and combined sets of physiological features (heartbeat and respiration dynamics, walking cadence, and cough frequency spectrum) at discriminating COVID-positive participants from the healthy group. Combining features yielded an AUC of 0.94 (95% CI=[0.92, 0.96]) using a leave-one-subject-out cross validation scheme. Conclusions and Clinical Impact: These results, although preliminary, suggest that a sensor-based snapshot paradigm may be a promising approach for non-invasive and repeatable testing to alert individuals that need further screening.

Starting at Birth: An Integrative, State-of-the-Science Framework for Optimizing Infant Neuromotor Health

Numerous conditions and circumstances place infants at risk for poor neuromotor health, yet many are unable to receive treatment until a definitive diagnosis is made, sometimes several years later. In this integrative perspective, we describe an extensive team science effort to develop a transdiagnostic approach to neuromotor health interventions designed to leverage the heightened neuroplasticity of the first year of life. We undertook the following processes: (1) conducted a review of the literature to extract common principles and strategies underlying effective neuromotor health interventions; (2) hosted a series of expert scientific exchange panels to discuss common principles, as well as practical considerations and/or lessons learned from application in the field; and (3) gathered feedback and input from diverse stakeholders including infant caregivers and healthcare providers. The resultant framework was a pragmatic, evidence-based, transdiagnostic approach to optimize neuromotor health for high-risk infants based on four principles: (a) active learning, (b) environmental enrichment, (c) caregiver engagement, and (d) strength-based approaches. In this perspective paper, we delineate these principles and their potential applications. Innovations include: engagement of multiple caregivers as critical drivers of the intervention; promoting neuromotor health in the vulnerability phase, rather than waiting to treat neuromotor disease; integrating best practices from adjacent fields; and employing a strengths-based approach. This framework holds promise for implementation as it is scalable, pragmatic, and holistically addresses both the needs of the infant and their family.

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.