Development of an activity-based therapy tracking tool: Item generation and reduction using the Delphi method.

CONTEXT/OBJECTIVE: Activity-based therapies (ABT) are increasingly used in rehabilitation after spinal cord injury or disease (SCI/D). However, the absence of standardized tools to track the details of an ABT program hinders the collection of data needed for client-tailored programming and resource allocation. The objective of this study is to determine the content to include in an ABT tracking tool for people living with SCI/D. DESIGN: Cross-sectional e-survey. SETTING: Community. PARTICIPANTS: The 60 participants from Canada and the United States who had knowledge and/or experience with ABT included: individuals with SCI/D; hospital clinicians (i.e. physical and occupational therapists/assistants); community-based clinicians; hospital or community clinic administrators; researchers; and funders, advocates and policy makers. INTERVENTIONS: None. OUTCOME MEASURES: A Delphi e-survey comprised 16 types of ABT (e.g. treadmill training) and 4 types of technology (e.g. virtual reality). Participants rated the importance of including each item on a tracking tool and the feasibility to track each item using a 9-point Likert scale. RESULTS: After two survey rounds, nine types of ABT and one technology were identified as important to include in a tracking tool. All items rated as important were considered feasible for clinicians and people with SCI/D to track, except crawling. CONCLUSION: This study identified the types of ABT and technology to include in an ABT tracking tool. Such a tool may provide details of an ABT program that can support decision-making at the individual, program and health system levels and aid the development of best practice guidelines.

Modulations in neural pathways excitability post transcutaneous spinal cord stimulation among individuals with spinal cord injury: a systematic review.

Introduction: Transcutaneous spinal cord stimulation (TSCS), a non-invasive form of spinal cord stimulation, has been shown to improve motor function in individuals living with spinal cord injury (SCI). However, the effects of different types of TSCS currents including direct current (DC-TSCS), alternating current (AC-TSCS), and spinal paired stimulation on the excitability of neural pathways have not been systematically investigated. The objective of this systematic review was to determine the effects of TSCS on the excitability of neural pathways in adults with non-progressive SCI at any level. Methods: The following databases were searched from their inception until June 2022: MEDLINE ALL, Embase, Web of Science, Cochrane Library, and clinical trials. A total of 4,431 abstracts were screened, and 23 articles were included. Results: Nineteen studies used TSCS at the thoracolumbar enlargement for lower limb rehabilitation (gait & balance) and four studies used cervical TSCS for upper limb rehabilitation. Sixteen studies measured spinal excitability by reporting different outcomes including Hoffmann reflex (H-reflex), flexion reflex excitability, spinal motor evoked potentials (SMEPs), cervicomedullay evoked potentials (CMEPs), and cutaneous-input-evoked muscle response. Seven studies measured corticospinal excitability using motor evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS), and one study measured somatosensory evoked potentials (SSEPs) following TSCS. Our findings indicated a decrease in the amplitude of H-reflex and long latency flexion reflex following AC-TSCS, alongside an increase in the amplitudes of SMEPs and CMEPs. Moreover, the application of the TSCS-TMS paired associative technique resulted in spinal reflex inhibition, manifested by reduced amplitudes in both the H-reflex and flexion reflex arc. In terms of corticospinal excitability, findings from 5 studies demonstrated an increase in the amplitude of MEPs linked to lower limb muscles following DC-TSCS, in addition to paired associative stimulation involving repetitive TMS on the brain and DC-TSCS on the spine. There was an observed improvement in the latency of SSEPs in a single study. Notably, the overall quality of evidence, assessed by the modified Downs and Black Quality assessment, was deemed poor. Discussion: This review unveils the systematic evidence supporting the potential of TSCS in reshaping both spinal and supraspinal neuronal circuitries post-SCI. Yet, it underscores the critical necessity for more rigorous, high-quality investigations.

Time series classification of multi-channel nerve cuff recordings using deep learning.

Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore function to individuals living with disabilities. While neurostimulation has been successfully translated into clinical use for several applications, it remains challenging to robustly collect and interpret neural recordings, especially for chronic applications. Nerve cuff electrodes offer a viable option for recording nerve signals, with long-term implantation success. However, nerve cuff electrodes' signals have low signal-to-noise ratios, resulting in reduced selectivity between neural pathways. The objective of this study was to determine whether deep learning techniques, specifically networks tailored for time series applications, can increase the recording selectivity achievable using multi-contact nerve cuff electrodes. We compared several neural network architectures, the impact and trade-off of window length on classification performance, and the benefit of data augmentation. Evaluation was carried out using a previously collected dataset of 56-channel nerve cuff recordings from the sciatic nerve of Long-Evans rats, which included afferent signals evoked using three types of mechanical stimuli. Through this study, the best model achieved an accuracy of 0.936 ± 0.084 and an F1-score of 0.917 ± 0.103, using 50 ms windows of data and an augmented training set. These results demonstrate the effectiveness of applying CNNs designed for time-series data to peripheral nerve recordings, and provide insights into the relationship between window duration and classification performance in this application.

Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning.

AIM: To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP). METHOD: Dual wrist-worn accelerometry data were collected from three females and seven males with unilateral CP (mean age = 10 years 2 months [SD 3 years]) while performing hand tasks during video-recorded play sessions. Video observers labelled instances of functional and non-functional hand use. Machine learning was compared to the conventional activity count approach for identifying unilateral hand movements as functional or non-functional. Correlation and agreement analyses compared the functional usage metrics derived from each method. RESULTS: The best-performing machine learning approach had high precision and recall when trained on an individual basis (F1 = 0.896 [SD 0.043]). On an individual basis, the best-performing classifier showed a significant correlation (r = 0.990, p < 0.001) and strong agreement (bias = 0.57%, 95% confidence interval = -4.98 to 6.13) with video observations. When validated in a leave-one-subject-out scenario, performance decreased significantly (F1 = 0.584 [SD 0.076]). The activity count approach failed to detect significant differences in non-functional or functional hand activity and showed no significant correlation or agreement with the video observations. INTERPRETATION: With further development, wearable accelerometry combined with machine learning may enable quantitative monitoring of everyday functional hand use in children with unilateral CP.

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface.

Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware. OBJECTIVE: To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings. METHODS: Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs). RESULTS: NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology. CONCLUSION: Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.

Tracking activity-based therapy for people living with spinal cord injury or disease: insights gained through focus group interviews with key stakeholders.

PURPOSE: The development of a tool to track participation in activity-based therapy (ABT) for people with spinal cord injury or disease (SCI/D) was identified as a priority of the Canadian ABT Community of Practice. The objective of this study was to understand multi-stakeholder perspectives on tracking ABT participation across the continuum of care. MATERIALS AND METHODS: Forty-eight individuals from six stakeholder groups (persons living with SCI/D; hospital therapists; community trainers; administrators; researchers; and funders, advocates and policy experts) were recruited to participate in focus group interviews. Participants were asked open-ended questions concerning the importance of and parameters around tracking ABT. Transcripts were analyzed using conventional content analysis. RESULTS: Themes reflected the Who, What, Where, When, Why and How of tracking ABT. Participants described the importance of involving hospital therapists, community trainers and individuals with SCI/D in tracking ABT to capture both subjective and objective parameters across the continuum of care and injury trajectory. Digital tracking tools were favoured, although paper-based versions were regarded as a necessity in some circumstances. CONCLUSIONS: Findings highlighted the importance of tracking ABT participation for individuals with SCI/D. The information may guide the development of ABT practice guidelines and support the implementation of ABT in Canada.

Tracking the details of activity-based therapy (ABT) sessions and programs across the continuum of care and injury trajectory may provide important information to support the development of ABT practice guidelines and implementation strategies.Tracking objective and subjective parameters are needed to provide a comprehensive description of an ABT session and program.Clinicians and individuals with spinal cord injury or disease (SCI/D) should both be able to track ABT to accommodate all settings and types of data.Digital tracking tools, such as an app, may provide an accessible, versatile and efficient way of tracking ABT.

Hand Grasp Classification in Egocentric Video After Cervical Spinal Cord Injury.

OBJECTIVE: The hand function of individuals with spinal cord injury (SCI) plays a crucial role in their independence and quality of life. Wearable cameras provide an opportunity to analyze hand function in non-clinical environments. Summarizing the video data and documenting dominant hand grasps and their usage frequency would allow clinicians to quickly and precisely analyze hand function. METHOD: We introduce a new hierarchical model to summarize the grasping strategies of individuals with SCI at home. The first level classifies hand-object interaction using hand-object contact estimation. We developed a new deep model in the second level by incorporating hand postures and hand-object contact points using contextual information. RESULTS: In the first hierarchical level, a mean of 86% ±1.0% was achieved among 17 participants. At the grasp classification level, the mean average accuracy was 66.2 ±12.9%. The grasp classifier's performance was highly dependent on the participants, with accuracy varying from 41% to 78%. The highest grasp classification accuracy was obtained for the model with smoothed grasp classification, using a ResNet50 backbone architecture for the contextual head and a temporal pose head. DISCUSSION: We introduce a novel algorithm that, for the first time, enables clinicians to analyze the quantity and type of hand movements in individuals with spinal cord injury at home. The algorithm can find applications in other research fields, including robotics, and most neurological diseases that affect hand function, notably, stroke and Parkinson's.

Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks.

BACKGROUND: It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Compound Action Potentials Extraction Network (ESCAPE-NET) is a convolutional neural network (CNN) that has previously been demonstrated to be effective at discriminating neural sources in rat sciatic nerves. ESCAPE-NET was designed to operate using data from multi-channel nerve cuff arrays, and use the resulting spatiotemporal signatures to classify individual naturally evoked compound action potentials (nCAPs) based on differing source fascicles. The applicability of this approach to larger and more complex nerves is not well understood. To support future translation to humans, the objective of this study was to characterize the performance of this approach in a computational model of the human median nerve. METHODS: Using a cross-sectional immunohistochemistry image of a human median nerve, a finite-element model was generated and used to simulate extraneural recordings. ESCAPE-NET was used to classify nCAPs based on source location, for varying numbers of sources and noise levels. The performance of ESCAPE-NET was also compared to ResNet-50 and MobileNet-V2 in the context of classifying human nerve cuff data. RESULTS: Classification accuracy was found to be inversely related to the number of nCAP sources in ESCAPE-NET (3-class: 97.8% ± 0.1%; 10-class: 89.3% ± 5.4% in low-noise conditions, 3-class: 70.3% ± 0.1%; 10-class: 52.5% ± 0.3% in high-noise conditions). ESCAPE-NET overall outperformed both MobileNet-V2 (3-class: 96.5% ± 1.1%; 10-class: 84.9% ± 1.7% in low-noise conditions, 3-class: 86.0% ± 0.6%; 10-class: 41.4% ± 0.9% in high-noise conditions) and ResNet-50 (3-class: 71.2% ± 18.6%; 10-class: 40.1% ± 22.5% in low-noise conditions, 3-class: 81.3% ± 4.4%; 10-class: 31.9% ± 4.4% in high-noise conditions). CONCLUSION: All three networks were found to learn to differentiate nCAPs from different sources, as evidenced by performance levels well above chance in all cases. ESCAPE-NET was found to have the most robust performance, despite decreasing performance as the number of classes increased, and as noise was varied. These results provide valuable translational guidelines for designing neural interfaces for human use.

Fascicle-Selective Ultrasound-Powered Bidirectional Wireless Peripheral Nerve Interface IC.

This paper presents an innovative, minimally invasive, battery-free, wireless, peripheral nervous system (PNS) neural interface, which seamlessly integrates a millimeter-scale, fascicle-selective integrated circuit (IC) with extraneural recording and stimulating channels. The system also incorporates a wearable interrogator equipped with integrated machine-learning capabilities. This PNS interface is specifically tailored for adaptive neuromodulation therapy, targeting individuals with paralysis, amputation, or chronic medical conditions. By employing a neural pathway classifier and temporal interference stimulation, the proposed interface achieves precise deep fascicle selectivity for recording and stimulation without the need for nerve penetration or compression. Ultrasonic energy harvesters facilitate wireless power harvesting and data reception, enhancing the usability of the system. Key circuit performance metrics encompass a 2.2 µVrms input-referred noise, 14-bit ENOB, and a 173 dB Schreier figure of merit (FOM) for the neural analog-to-digital converter (ADC). Additionally, the ultra-low-power radio-frequency (RF) transmitter boasts a remarkable 1.38 pJ/bit energy efficiency. In vivo experiments conducted on rat sciatic nerves provide compelling evidence of the interface's ability to selectively stimulate and record neural fascicles. The proposed PNS neural interface offers alternative treatment options for diagnosing and treating neurological disorders, as well as restoring or repairing neural functions, improving the quality of life for patients with neurological and sensory deficits.

A computational model of surface electromyography signal alterations after spinal cord injury.

Objective. Spinal cord injury (SCI) can cause significant impairment and disability with an impact on the quality of life for individuals with SCI and their caregivers. Surface electromyography (sEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing neuromuscular changes resulting from SCI. The mechanisms of the sEMG signal characteristic changes due to SCI are multi-faceted and difficult to studyin vivo. In this study, we utilized well-established computational models to characterize changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the SCI.Approach. Starting from existing models for motor neuron pool organization and motor unit action potential generation for healthy neuromuscular systems, we implemented scenarios to model damages to upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit. After simulating sEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on sEMG features using the Kendall Rank Correlation analysis.Main results. The commonly used amplitude-based sEMG features (such as mean absolute values and root mean square) cannot differentiate between injury scenarios, but a broader set of features (including autoregression and cepstrum coefficients) provides greater specificity to the type of damage present.Significance. We introduce a novel approach to mechanistically relate sEMG features (often underused in SCI research) to different types of neuromuscular alterations that may occur after SCI. This work contributes to the further understanding and utilization of sEMG in clinical applications, which will ultimately improve patient outcomes after SCI.

Recognizing hand use and hand role at home after stroke from egocentric video.

Hand function is a central determinant of independence after stroke. Measuring hand use in the home environment is necessary to evaluate the impact of new interventions, and calls for novel wearable technologies. Egocentric video can capture hand-object interactions in context, as well as show how more-affected hands are used during bilateral tasks (for stabilization or manipulation). Automated methods are required to extract this information. The objective of this study was to use artificial intelligence-based computer vision to classify hand use and hand role from egocentric videos recorded at home after stroke. Twenty-one stroke survivors participated in the study. A random forest classifier, a SlowFast neural network, and the Hand Object Detector neural network were applied to identify hand use and hand role at home. Leave-One-Subject-Out-Cross-Validation (LOSOCV) was used to evaluate the performance of the three models. Between-group differences of the models were calculated based on the Mathews correlation coefficient (MCC). For hand use detection, the Hand Object Detector had significantly higher performance than the other models. The macro average MCCs using this model in the LOSOCV were 0.50 ± 0.23 for the more-affected hands and 0.58 ± 0.18 for the less-affected hands. Hand role classification had macro average MCCs in the LOSOCV that were close to zero for all models. Using egocentric video to capture the hand use of stroke survivors at home is technically feasible. Pose estimation to track finger movements may be beneficial to classifying hand roles in the future.

Grasp Analysis in the Home Environment as a Measure of Hand Function After Cervical Spinal Cord Injury.

BACKGROUND: Following a spinal cord injury, regaining hand function is a top priority. Current hand assessments are conducted in clinics, which may not fully represent real-world hand function. Grasp strategies used in the home environment are an important consideration when examining the impact of rehabilitation interventions. OBJECTIVE: The main objective of this study is to investigate the relationship between grasp use at home and clinical scores. METHOD: We used a previously collected dataset in which 21 individuals with spinal cord injuries (SCI) recorded egocentric video while performing activities of daily living in their homes. We manually annotated 4432 hand-object interactions into power, precision, intermediate, and non-prehensile grasps. We examined the distributions of grasp types used and their relationships with clinical assessments. RESULTS: Moderate to strong correlations were obtained between reliance on power grasp and the Spinal Cord Independence Measure III (SCIM; P < .05), the upper extremity motor score (UEMS; P < .01), and the Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP) Prehension (P < .01) and Strength (P < .01). Negative correlations were observed between the proportion of non-prehensile grasping and SCIM (P < .05), UEMS (P < .05), and GRASSP Prehension (P < .01) and Strength (P < .01). CONCLUSION: The types of grasp types used in naturalistic activities at home are related to upper limb impairment after cervical SCI. This study provides the first direct demonstration of the importance of hand grasp analysis in the home environment.

Manipulating facial musculature with functional electrical stimulation as an intervention for major depressive disorder: a focused search of literature for a proposal.

BACKGROUND: Major Depressive Disorder (MDD) is associated with interoceptive deficits expressed throughout the body, particularly the facial musculature. According to the facial feedback hypothesis, afferent feedback from the facial muscles suffices to alter the emotional experience. Thus, manipulating the facial muscles could provide a new "mind-body" intervention for MDD. This article provides a conceptual overview of functional electrical stimulation (FES), a novel neuromodulation-based treatment modality that can be potentially used in the treatment of disorders of disrupted brain connectivity, such as MDD. METHODS: A focused literature search was performed for clinical studies of FES as a modulatory treatment for mood symptoms. The literature is reviewed in a narrative format, integrating theories of emotion, facial expression, and MDD. RESULTS: A rich body of literature on FES supports the notion that peripheral muscle manipulation in patients with stroke or spinal cord injury may enhance central neuroplasticity, restoring lost sensorimotor function. These neuroplastic effects suggest that FES may be a promising innovative intervention for psychiatric disorders of disrupted brain connectivity, such as MDD. Recent pilot data on repetitive FES applied to the facial muscles in healthy participants and patients with MDD show early promise, suggesting that FES may attenuate the negative interoceptive bias associated with MDD by enhancing positive facial feedback. Neurobiologically, the amygdala and nodes of the emotion-to-motor transformation loop may serve as potential neural targets for facial FES in MDD, as they integrate proprioceptive and interoceptive inputs from muscles of facial expression and fine-tune their motor output in line with socio-emotional context. CONCLUSIONS: Manipulating facial muscles may represent a mechanistically novel treatment strategy for MDD and other disorders of disrupted brain connectivity that is worthy of investigation in phase II/III trials.

Functional electrical stimulation therapy for upper extremity rehabilitation following spinal cord injury: a pilot study.

STUDY DESIGN: Pilot study. OBJECTIVES: To examine if functional electrical stimulation therapy (FEST) improves neuromuscular factors underlying upper limb function in individuals with SCI. SETTING: A tertiary spinal cord rehabilitation center specialized in spinal cord injury care in Canada. METHODS: We examined 29 muscles from 4 individuals living with chronic, cervical, and incomplete SCI. The analysis was focused on the changes in muscle activation, as well as on how the treatment could change the ability to control a given muscle or on how multiple muscles would be coordinated during volitional efforts. RESULTS: There was evidence of gains in muscle strength, activation, and median frequency after the FEST. Gains in muscle activation indicated the activation of a greater number of motor units and gains in muscle median frequency the involvement of higher threshold, faster motor units. In some individuals, these changes were smaller but accompanied by increased control over muscle contraction, evident in a greater ability to sustain a volitional contraction, reduce the co-contraction of antagonist muscles, and provide cortical drive. CONCLUSIONS: FEST increases muscle strength and activation. Enhanced control of muscle contraction, reduced co-contraction of antagonist muscles, and a greater presence of cortical drive were some of the findings supporting the effects of FEST at the sensory-motor integration level.

Validity of Novel Outcome Measures for Hand Function Performance After Stroke Using Egocentric Video.

BACKGROUND: Evaluating upper limb (UL) interventions after stroke calls for outcome measures that describe impact on daily life in the community. UL use ratio has been used to quantify the performance domain of UL function, but generally focuses on arm use only. A hand use ratio could provide additional information about UL function after stroke. Additionally, a ratio based on the role of the more-affected hand in bilateral activities (stabilizer or manipulator) may also reflect hand function recovery. Egocentric video is a novel modality that can record both dynamic and static hand use and hand roles at home after stroke. OBJECTIVE: To validate hand use and hand role ratios from egocentric video against standardized clinical UL assessments. METHODS: Twenty-four stroke survivors recorded daily tasks in a home simulation laboratory and their daily routines at home using egocentric cameras. Spearman's correlation was used to compare the ratios with the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and Motor Activity Log-30 (MAL, Amount of Use (AoU), and Quality of Movement (QoM)). RESULTS: Hand use ratio significantly correlated with the FMA-UE (0.60, 95% CI: 0.26, 0.81), ARAT (0.44, CI: 0.04, 0.72), MAL-AoU (0.80, CI: 0.59, 0.91), and MAL-QoM (0.79, CI: 0.57, 0.91). Hand role ratio had no significant correlations with the assessments. CONCLUSION: Hand use ratio automatically extracted from egocentric video, but not hand role ratio, was found to be a valid measure of hand function performance in our sample. Further investigation is necessary to interpret hand role information.

Segmental motor recovery after cervical spinal cord injury relates to density and integrity of corticospinal tract projections.

Cervical spinal cord injury (SCI) causes extensive impairments for individuals which may include dextrous hand function. Although prior work has focused on the recovery at the person-level, the factors determining the recovery of individual muscles are poorly understood. Here, we investigate the muscle-specific recovery after cervical spinal cord injury in a retrospective analysis of 748 individuals from the European Multicenter Study about Spinal Cord Injury (NCT01571531). We show associations between corticospinal tract (CST) sparing and upper extremity recovery in SCI, which improves the prediction of hand muscle strength recovery. Our findings suggest that assessment strategies for muscle-specific motor recovery in acute spinal cord injury are improved by accounting for CST sparing, and complement person-level predictions.

Analysis of the Hands in Egocentric Vision: A Survey.

Egocentric vision (a.k.a. first-person vision-FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.

Using activity-based therapy for individuals with spinal cord injury or disease: Interviews with physical and occupational therapists in rehabilitation hospitals.

OBJECTIVE: To understand if and how physical therapists (PTs) and occupational therapists (OTs) use activity-based therapy (ABT) and its associated technologies for the rehabilitation of individuals living with spinal cord injury or disease (SCI/D) in Canadian rehabilitation hospital settings. DESIGN: Qualitative study. SETTING: Through rehabilitation hospitals participating in the Rick Hansen Spinal Cord Injury Registry, we recruited licensed OTs and PTs to participate in focus groups. PARTICIPANTS: Twelve PTs and ten OTs from nine sites across eight provinces participated. OUTCOME MEASURES: To inform the development of a semi-structured interview guide, we used the Theoretical Domains Framework. To analyze the data, we used interpretive description. RESULTS: We identified three themes that influenced therapists' use of ABT and associated technologies for SCI/D rehabilitation. (1) Therapists' decision-making approach to ABT and technology. Therapist roles, site-specific dynamics and goal setting influenced decision-making. Assuming roles such as mentor, liaison and advocate led to more ABT use. Site-specific dynamics concerned levels of ABT knowledge, teamwork, and staffing. In hospital rehabilitation, there was competition between discharge and neurorecovery goals. (2) Therapist perceived individual factors. Patient factors either increased (i.e. patients' motivation, self-advocacy) or prevented (i.e. mourning period, tolerance) the likelihood that ABT was introduced by therapists. (3) ABT and equipment access. Technology was used for ABT in a variety of ways. Access was affected by visible (e.g. equipment cost) and invisible barriers (e.g. departmental relations). CONCLUSIONS: The use of ABT and its associated technologies in Canadian rehabilitation hospitals is variable. Ongoing education could be offered, and site-specific implementation strategies could be developed, to promote ABT use.

Providing Insights into the Challenges of Implementing Activity-Based Therapy in Canada: A Comparative Analysis Using Focus Group Interviews with Key Interest Groups.

Background: Activity-based therapy (ABT) has emerged as a therapeutic approach that may promote neurorecovery and reduce secondary complications in people living with spinal cord injury or disease (SCI/D). In spite of the numerous health benefits, adoption of ABT into practice has been limited across the Canadian care continuum. Objectives: This study aimed to understand the challenges of implementing ABT in Canada for people living with SCI/D through the perspectives of key interest groups. Methods: Researchers, hospital therapists, community trainers, administrators, persons living with SCI/D, and advocates, funders, and policy experts who had knowledge of and/or experience with ABT participated in focus group interviews to share their perspectives on the barriers to ABT practice. Interviews were analyzed using conventional content analysis followed by a comparative analysis across groups. Results: The 48 participants identified six key challenges: (1) challenge of gaps in knowledge/training, (2) challenge of standardizing ABT, (3) challenge of determining the optimal timing of ABT, (4) challenge of defining, characterizing, and achieving high dosage and intensity, (5) challenge of funding ABT, and (6) challenge of measuring participation and performance in ABT. A comparative analysis found some challenges were emphasized by certain groups, such as the cost of ABT for persons with SCI/D, lack of education and training in ABT for therapists and trainers, minimal evidence to develop guidelines for researchers and advocates, and funding ABT programs for administrators. Conclusion: Participants highlighted several challenges that limit ABT practice. Strategies to address these challenges will support successful implementation of ABT in Canada.