Semi-Supervised Behavior Labeling Using Multimodal Data during Virtual Teamwork-Based Collaborative Activities.

Adaptive human-computer systems require the recognition of human behavior states to provide real-time feedback to scaffold skill learning. These systems are being researched extensively for intervention and training in individuals with autism spectrum disorder (ASD). Autistic individuals are prone to social communication and behavioral differences that contribute to their high rate of unemployment. Teamwork training, which is beneficial for all people, can be a pivotal step in securing employment for these individuals. To broaden the reach of the training, virtual reality is a good option. However, adaptive virtual reality systems require real-time detection of behavior. Manual labeling of data is time-consuming and resource-intensive, making automated data annotation essential. In this paper, we propose a semi-supervised machine learning method to supplement manual data labeling of multimodal data in a collaborative virtual environment (CVE) used to train teamwork skills. With as little as 2.5% of the data manually labeled, the proposed semi-supervised learning model predicted labels for the remaining unlabeled data with an average accuracy of 81.3%, validating the use of semi-supervised learning to predict human behavior.

SAR-Connect: A Socially Assistive Robotic System to Support Activity and Social Engagement of Older Adults.

Multi-domain activities that incorporate physical, cognitive, and social stimuli can enhance older adults' overall health and quality of life. Several robotic platforms have been developed to provide these therapies in a quantifiable manner to complement healthcare personnel in resource-strapped long-term care settings. However, these platforms are primarily limited to one-to-one human robot interaction (HRI) and thus do not enhance social interaction. In this paper, we present a novel HRI framework and a realized platform called SAR-Connect to foster robot-mediated social interaction among older adults through carefully designed tasks that also incorporate physical and cognitive stimuli. SAR-Connect seamlessly integrates a humanoid robot with a virtual reality-based activity platform and a multimodal data acquisition module including game interaction, audio, visual and electroencephalography responses of the participants. Results from a laboratory-based user study with older adults indicates the potential of SAR-Connect that showed this system could 1) involve one or multiple older adults to perform multi-domain activities and provide dynamic guidance, 2) engage them in the robot-mediated task and foster human-human interaction, and 3) quantify their social and activity engagement from multiple sensory modalities.

Use of robots to encourage social engagement between older adults.

We designed a robotic architecture system within a commercially available socially assistive robot to engage pairs of older adults in multimodal activities over 3 weeks for 6 sessions. The study took place in two assisted living facilities. Seven pairs (14 individuals) completed the experiment. Ages ranged from 70 to 90 years with a mean age of 83.0 (± 6.1). Most were women (79%). Three adults were screened as having normal cognition, 10 had mild cognitive impairment, and 1 adult self-reported a diagnosis of Alzheimer's disease. All sessions were video recorded and analyzed using Noldus Observer XT. Individuals demonstrated high levels of both human-human interaction and human-robot interaction, but the activity influenced the type of interaction. Engagement measures (visual, verbal, behavioral) also varied by type of activity. Future studies will focus on further development of activities that can engage older adults with varying levels of cognitive impairment and apathy.

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC).

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

Socially Assistive Robots: Measuring Older Adults' Perceptions.

To address manpower shortages, health care leaders recommend technology, including robots, to facilitate and augment processes for delivery of efficient, safe care. Little is known regarding older adults' perceptions of socially assistive robots (SARs). Using the Unified Theory of Acceptance and Use Technology framework, a survey was developed and tested for capturing older adults' likelihood to use SARs. The Robot Acceptance Survey (RAS) comprises three subscales: Performance Expectancy, Effort Expectancy, and Attitude. Older adults completed the RAS pre- and post-experimental procedure with a SAR. Cronbach's alpha coefficients for the subscales ranged from 0.77 to 0.89. Subscales were sensitive to change, with more positive reactions after exposure to SAR activities. Future studies must identify robotic programming capable of providing cognitive, physical, and social assistance, as well as person-, activity-, situation-, and robot-specific factors that will influence older adults' acceptance of SARs. [Journal of Gerontological Nursing, 43(12), 35-43.].

Design and Evaluation of an Actuated Exoskeleton for Examining Motor Control in Stroke Thumb.

Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in examining motor control and hand rehabilitation. The ATX presented in this work aims to provide independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a novel flexible shaft based mechanism that has 5 active DOF and 3 passive DOF. A prototype has been built and experiments have been conducted to measure the allowable workspace at the thumb and evaluate the kinematic and kinetic performance of the ATX. The experimental results show that the ATX is able to provide individual actuation at all 5 thumb joints with high joint velocity and torque capacities. Further improvement and future work are discussed.

Network of movement and proximity sensors for monitoring upper-extremity motor activity after stroke: proof of principle.

OBJECTIVE: To test the convergent validity of an objective method, Sensor-Enabled Radio-frequency Identification System for Monitoring Arm Activity (SERSMAA), that distinguishes between functional and nonfunctional activity. DESIGN: Cross-sectional study. SETTING: Laboratory. PARTICIPANTS: Participants (N=25) were ≥0.2 years poststroke (median, 9) with a wide range of severity of upper-extremity hemiparesis. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: After stroke, laboratory tests of the motor capacity of the more-affected arm poorly predict spontaneous use of that arm in daily life. However, available subjective methods for measuring everyday arm use are vulnerable to self-report biases, whereas available objective methods only provide information on the amount of activity without regard to its relation with function. The SERSMAA consists of a proximity-sensor receiver on the more-affected arm and multiple units placed on objects. Functional activity is signaled when the more-affected arm is close to an object that is moved. Participants were videotaped during a laboratory simulation of an everyday activity, that is, setting a table with cups, bowls, and plates instrumented with transmitters. Observers independently coded the videos in 2-second blocks with a validated system for classifying more-affected arm activity. RESULTS: There was a strong correlation (r=.87, P<.001) between time that the more-affected arm was used for handling objects according to the SERSMAA and functional activity according to the observers. CONCLUSIONS: The convergent validity of SERSMAA for measuring more-affected arm functional activity after stroke was supported in a simulation of everyday activity.

Design and Development of the Cable Actuated Finger Exoskeleton for Hand Rehabilitation Following Stroke.

Finger impairment following stroke results in significant deficits in hand manipulation and the performance of everyday tasks. While recent advances in rehabilitation robotics have shown promise for facilitating functional improvement, it remains unclear how best to employ these devices to maximize benefits. Current devices for the hand, however, lack the capacity to fully explore the space of possible training paradigms. Particularly, they cannot provide the independent joint control and levels of velocity and torque required. To fill this need, we have developed a prototype for one digit, the cable actuated finger exoskeleton (CAFE), a three-degree-of-freedom robotic exoskeleton for the index finger. This paper presents the design and development of the CAFE, with performance testing results.

Can surface EMG be adequately described by digital sampling?

Surface electromyography (SEMG) is a common tool to evaluate muscle function in kinesiological studies, musculoskeletal rehabilitation, prosthetics, clinical research and neurological disease diagnosis. The acquisition of SEMG is a crucially basic issue to gain an insight into musculoskeletal system function. The aim of this study is to investigate if the sampled surface EMG signals can reflect adequately the neural activity of the underlying musculature. The surface EMG signals of four muscles (abductor pollicis muscles and abductor digiti minimi muscles of right hand and left hand) are studied on the amplitude, frequency and nonlinear measure based on symplectic geometry. There are obvious differences in nonlinear measures of the different sampled signals, although there are little significant changes in their amplitude and frequency measures. Meanwhile, surface EMG signals obviously differ from their surrogate data at higher sampling frequencies. The results indicate that surface EMG signals contain nonlinear components. To gather the sufficient information of surface EMG signal, the data acquisition should be required at the higher sampling frequency. Furthermore, the nonlinear measure based on symplectic geometry can be used as a sensitive index for evaluation of the activity of the human muscles.

Every "Body" Gets a Say: An Augmented Optimization Metric to Preserve Body Pose during Avatar Adaptation in Mixed/Augmented Reality.

User-Avatar interaction within augmented reality applications is rapidly increasing in frequency. Applications routinely place users in rooms with other, remote users embodied by photorealistic avatars, or require users to work with an avatar of a remote user to complete a task. During these types of interactions, it is often required to modify or redirect the posture of an avatar to achieve goals such as contact with or pointing at an object or maintaining eye gaze with the local user. A key limitation of modern redirection techniques is successfully preserving body posture, a critical component of nonverbal communication. This paper presents a new pose-preserving objective function to be used in the multi-objective optimization of an avatar's kinematic configuration. This objective function not only mimics the correct placement of body joints, but also preserves their orientation in space. We have tested this approach against several commonly used and current state-of-the-art redirection techniques and have found that our new approach achieves a significant reduction in targeted redirection error while simultaneously reducing body posture error. Additionally, human subject testing has shown that our new technique provides both a significantly more natural looking redirection and a significantly more realistic and believable overall body posture.

Unusual Presentation of Reversible Transient Vision Loss After Caesarean Section Under Subarachnoid Block: A Case Report.

A transient vision loss is not commonly encountered during the postoperative period following a caesarean section. Although numerous causes have been suggested for transient vision loss, when loss of vision is associated with seizures and headaches, the differential diagnoses include hemolysis, elevated liver enzymes, low platelet syndrome, reversible cerebral vasoconstriction syndrome, posterior reversible encephalopathy syndrome (PRES), dural venous thrombosis, and central retinal arteriolar occlusion. We report a case of a 35-year-old patient who underwent an elective caesarean section under spinal anaesthesia and developed a headache followed by loss of vision and seizures during the postoperative period. An MRI scan of the brain on the same day revealed subtle hyperintensity in bilateral parieto-occipital lobes in the cortical and subcortical areas and bilateral cerebral hemispheres, which indicates PRES. Rapid and complete resolution of symptoms was observed with supportive treatment. Therefore, prompt suspicion and effective management of PRES are of paramount importance to prevent short- and long-term neurological deficits.

Multimodal job interview simulator for training of autistic individuals.

Autistic individuals face difficulties in finding and maintaining employment, and studies have shown that the job interview is often a significant barrier to obtaining employment. Prior computer-based job interview training interventions for autistic individuals have been associated with better interview outcomes. These previous interventions, however, do not leverage the use of multimodal data that could give insight into the emotional underpinnings of autistic individuals' challenges in job interviews. In this article, the authors present the design of a novel multimodal job interview training platform called CIRVR that simulates job interviews through spoken interaction and collects eye gaze, facial expressions, and physiological responses of the participants to understand their stress response and their affective state. Results from a feasibility study with 23 autistic participants who interacted with CIRVR are presented. In addition, qualitative feedback was gathered from stakeholders on visualizations of data on CIRVR's visualization tool called the Dashboard. The data gathered indicate the potential of CIRVR along with the Dashboard to be used in the creation of individualized job interview training of autistic individuals.

Sliding Mode Control of Steerable Needles.

Steerable needles can potentially increase the accuracy of needle-based diagnosis and therapy delivery, provided they can be adequately controlled based on medical image information. We propose a novel sliding mode control law that can be used to deliver the tip of a flexible asymmetric-tipped needle to a desired point, or to track a desired trajectory within tissue. The proposed control strategy requires no a priori knowledge of model parameters, has bounded input speeds, and requires little computational resources. We show that if the standard nonholonomic model for tip-steered needles holds, then the control law will converge to desired targets in a reachable workspace, within a tolerance that can be defined by the control parameters. Experimental results validate the control law for target points and trajectory following in phantom tissue and ex vivo liver. Experiments with targets that move during insertion illustrate robustness to disturbances caused by tissue deformation.

A Profile Study of Japanese Encephalitis in an Industrial Hospital in Eastern India.

Introduction Japanese encephalitis (JE), caused by a Flavivirus, is one of the common causes of mosquito-borne encephalitis the world over including India. The disease is endemic in many states of India, including Jharkhand. Mortality ranges from 30 to 40% in different studies and a large number of patients survive with permanent neuropsychiatric sequelae. Aim The study aimed to evaluate the clinical spectrum, laboratory (including radiological) features and outcomes of cases of JE admitted in our hospital. Methods and materials This is a retrospective observational study consisting of confirmed cases of JE admitted to the medical wards and critical care unit of Tata Main Hospital (TMH) from January to December 2022. The case records of patients were retrieved from Hospital Management System (HMS) and analysed for demographic characteristics, clinical presentations and treatment details along with outcome measures, which included length of stay (LOS), complications, and mortality. Observation Of the 14 confirmed cases, six (43.9%) were males and eight (57.1%) were females. The average age of male and female patients was 41.8 ± 23.1 and 35.1 ± 20.5 years respectively. A total of 35.7% of the patients were in the age group of 21 to 30 years. The clinical manifestations in the decreasing frequency were altered sensorium in 11 (78.6%) patients, headache in six (42.8%) patients, generalised convulsions in four (28.6%) cases, vomiting in three (21.4%) cases and weakness in all limbs and of the right half of the body in one (7.1%) case each. Objective neurological findings noted were neck stiffness (3, 21.4%), cog-wheel rigidity (3, 21.4%), tremors (2, 14.3%), delirium (2,14.3%), quadriparesis, facio-brachial dystonia and hemiparesis (1, 7.14%) patient each. Neutrophilic leucocytosis was observed in five (35.7%) patients and mild thrombocytopenia in two (14.3%) patients. The average C-reactive protein (CRP) level was 7.3 ± 6.6 mg/dL. Three (21.4%) patients had mild transaminitis. Cerebrospinal fluid analysis was abnormal in all patients with varying degrees of elevated protein and cell count, while adenosine deaminase (ADA) levels and sugar were normal in all patients. Magnetic resonance imaging (MRI) brain revealed bilateral thalamic T2 FLAIR (fluid-attenuated inversion recovery) hyperintensities in 11 patients (78.6%). The average length of hospital stay was 9.6 ± 4.7 days. Ten patients (71.4%) needed treatment in the critical care unit. Complications seen were acute respiratory distress syndrome (2, 14.3%), sepsis with multiorgan failure (2, 14.3%) and ventilator-associated pneumonia (1, 7.1%). The case fatality rate was one (7.1%). Six patients (42.9%) had residual neuropsychiatric sequelae. Conclusion JE continues to be a major health-related problem. Most cases are concentrated during the post-monsoon period, coinciding with a higher vector density. Patients from rural backgrounds were seen to be more susceptible. JE may present with varying severities of acute encephalitic syndrome. As there is no specific treatment, timely diagnosis is important to reduce the morbidity and mortality associated with this disease. Clinicians must be aware of the wide spectrum of presentation of this disease. A high degree of suspicion along with thorough clinical examination and appropriate investigations are needed to diagnose this condition early and prevent complications.

Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study.

Sleep is vital to many processes involved in the well-being and health of children; however, it is estimated that 80% of children with Rett syndrome suffer from sleep disorders. Caregiver reports and questionnaires, which are the current method of studying sleep, are prone to observer bias and missed information. Polysomnography is considered the gold standard for sleep analysis but is labor and cost-intensive and limits the frequency of data collection for sleep disorder studies. Wearable digital health technologies, such as actigraphy devices, have shown potential and feasibility as a method for sleep analysis in Rett syndrome, but have not been validated against polysomnography. Furthermore, the collected accelerometer data has limitations due to the rigidity, periodic limb movement, and involuntary muscle contractions prevalent in Rett syndrome. Heart rate and electrodermal activity, along with other physiological signals, have been linked to sleep stages and can be utilized with machine learning to provide better resistance to noise and false positives than actigraphy. This research aims to address the gap in Rett syndrome sleep analysis by comparing the performance of a machine learning model utilizing both accelerometer data and physiological data features to the gold-standard polysomnography for sleep analysis in Rett syndrome. Our analytical validation pilot study (n = 7) found that using physiological and accelerometer features, our machine learning models can differentiate between awake, non-rapid eye movement sleep, and rapid eye movement sleep in Rett syndrome children with an accuracy of 85.1% when using an individual model. Additionally, this work demonstrates that it is feasible to use digital health technologies in Rett syndrome, even at a young age, without data loss or interference from repetitive movements that are characteristic of Rett syndrome.

Development of an App for Tracking Family Engagement With Early Intervention Services: Focus Groups and Pilot Evaluation Study.

BACKGROUND: Expedient access to early intervention (EI) systems has been identified as a priority for children with developmental delays, identified disabilities, and other special health care needs. Despite the mandated availability of EI, it remains challenging for families to navigate referral processes and establish appropriate services. Such challenges disproportionately affect families from traditionally underserved communities. Mobile health apps can improve clinical outcomes, increase accessibility to health services, and promote adherence to health-related interventions. Though promising, the implementation of apps within routine care is in its infancy, with limited research examining the components of what makes an effective app or how to reach families most impacted by inequities in health care delivery. OBJECTIVE: In study 1, we conducted focus groups to access a broad range of perspectives on the process of navigating the EI system, with the dual goals of identifying ways in which a patient-facing app might facilitate this process and identifying barriers to use with traditionally underrepresented and underserved groups. In study 2, focus group findings informed the development of a patient-facing app, which was subsequently tested with a pilot sample of 5 families. METHODS: In study 1, the focus groups included 29 participants from 4 shareholder groups. Targeted sampling was used to recruit participants from traditionally underrepresented groups. Focus group questions sought information about barriers families experience as they navigate the EI system, ideal features of a patient-facing app designed to track family engagement with the EI system, and potential barriers. Focus group procedures were informed by the Consolidated Framework for Implementation Research framework. In study 2, a pilot app was developed. The app was tested with a sample of 5 families of young children involved in the EI system. Families provided information on app functionality and usability. RESULTS: Qualitative analysis revealed a desire for increased communication and information about the process of accessing EI services, potential utility of an app for communication purposes, and clear recommendations for app features. Insights from focus groups were used to inform the development of the Family on Track app and related implementation supports. App features included survey customization, timing and delivery of prompts, and questions related to barriers and service satisfaction. Implementation supports include a visual guide for app installation, resources related to common family questions, and availability of study personnel to guide families through installation and provide ongoing support. Field testing provided preliminary information about app usability, including identifying future directions. CONCLUSIONS: The results of this study could support the development of a new way for the EI system to communicate and connect with families, provide families with a means to communicate satisfaction and frustration, and access the supports they need to be active participants in their child's care.

Design of a Desktop Virtual Reality-Based Collaborative Activities Simulator (ViRCAS) to Support Teamwork in Workplace Settings for Autistic Adults.

Autistic adults possess many skills sought by employers, but may be at a disadvantage in the workplace if social-communication differences negatively impact teamwork. We present a novel collaborative virtual reality (VR)-based activities simulator, called ViRCAS, that allows autistic and neurotypical adults to work together in a shared virtual space, offering the chance to practice teamwork and assess progress. ViRCAS has three main contributions: 1) a new collaborative teamwork skills practice platform; 2) a stakeholder-driven collaborative task set with embedded collaboration strategies; and 3) a framework for multimodal data analysis to assess skills. Our feasibility study with 12 participant pairs showed preliminary acceptance of ViRCAS, a positive impact of the collaborative tasks on supported teamwork skills practice for autistic and neurotypical individuals, and promising potential to quantitatively assess collaboration through multimodal data analysis. The current work paves the way for longitudinal studies that will assess whether the collaborative teamwork skill practice that ViRCAS provides also contributes towards improved task performance.

Paisley: Preliminary validation of a novel app-based e-Screener for ASD in children 18-36 months.

The purpose of this study was to assess the validity of an autism e-screener, Paisley, when utilized in a clinical research setting via a tablet application. The Paisley application used a series of play-based activities, all of which incorporated varying aspects of the ASD-PEDS. Participants included children (18-36 months; n = 198) referred for evaluation of autism spectrum disorder (ASD) and community providers (n = 66) with differing levels of familiarity with ASD. Community providers administered the Paisley application to children who then completed a comprehensive psychological evaluation. Based on comprehensive evaluation, 75% of children met diagnostic criteria for ASD. Paisley scores were significantly higher for children diagnosed with ASD (15.06) versus those not diagnosed (9.34). The newly determined cutoff ASD-PEDS cutoff score of 13 had significantly higher specificity and positive predictive value than the originally proposed cutoff of 11. Results support the use of Paisley by community providers to identify autism risk in toddlers. Limitations and strengths of the work, as well as opportunities for future clinical validation, are described.

Radiological Diagnosis of a Rare Prepontine Lesion: Ecchordosis Physaliphora.

Ecchordosis physaliphora (EP) is a notochordal remnant tissue rarely encountered during routine clinical practice. These lesions usually do not produce any significant symptoms as they are slow-growing and mostly small in size. Symptoms are due to mass effects on adjacent structures when they are large or extra-tumoral hemorrhage. Because of histological similarity with chordoma, diagnosis is challenging, and this differentiation is essential as the disease course and treatment differ significantly. Imaging plays a crucial role in identifying and distinguishing these lesions. We report the case of a 16-year-old male who presented with intermittent headache and neck pain for six months. His routine clinical examinations were within normal limits. On neurological assessment, there was no focal neurodeficit. Evaluation of cranial nerves did not reveal any evidence of palsy. Routine hematological tests were also normal. A computed tomography (CT) scan of the brain revealed a mass in front of the pons. Magnetic resonance imaging (MRI) for further evaluation revealed a T1 hypointense and T2/fluid-attenuated inversion recovery hyperintense lesion in the pre-pontine cistern. There was no enhancement in the mass either in the post-contrast CT or MRI scans. There was no bony erosion and clivus was normal. Based on the location and characteristic imaging features, a diagnosis of EP was made. There may be several other lesions that may present as a mass in the pre-pontine region. Histopathological tests may find it difficult to distinguish between lesions that originate from notochord remnants. Imaging studies play a vital role in confirming the diagnosis and help in planning treatment and follow-up.

Validating a Computerized Program for Supporting Visual Analysis During Functional Analysis: The Problem Behavior Multilevel Interpreter (PB.MI).

Computerized programs have been specifically developed in the field of applied behavior analysis for the purpose of automating data collection. Although they can potentially improve practicality of data collection for applied researchers and clinicians, program features of existing computerized programs do not include graphs and data interpretation generated in real time. We developed the Problem Behavior Multilevel Interpreter (PB.MI), which is designed to (a) allow for ongoing visual analysis of data displayed in real time and (b) support visual analysis with a computerized interpretation of functional control. The program was intended to be used during the functional analysis of problem behavior, specifically the single-session, interview-informed synthesized contingency analysis. In this article, we describe the program's functioning abilities and how we validated those abilities. In addition, we discuss the PB.MI program's practical utility. Supplementary Information: The online version contains supplementary material available at 10.1007/s40617-021-00656-7.