Strengthening cardiac therapy pipelines using human pluripotent stem cell-derived cardiomyocytes.

Advances in hiPSC isolation and reprogramming and hPSC-CM differentiation have prompted their therapeutic application and utilization for evaluating potential cardiovascular safety liabilities. In this perspective, we showcase key efforts toward the large-scale production of hiPSC-CMs, implementation of hiPSC-CMs in industry settings, and recent clinical applications of this technology. The key observations are a need for traceable gender and ethnically diverse hiPSC lines, approaches to reduce cost of scale-up, accessible clinical trial datasets, and transparent guidelines surrounding the safety and efficacy of hiPSC-based therapies.

Digital remote monitoring for screening and early detection of urinary tract infections.

Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.

Transferring Gait Predictors Across EMG Acquisition Systems with Domain Adaptation.

Lower limb assistive technology (e.g. exoskeletons) can benefit significantly from higher resolution information related to physiological state. High-density electromyography (HD-EMG) grids offer valuable spatial information on muscle activity; however their hardware is impractical, and bipolar electrodes remain the standard in practice. Exploiting information rich HD-EMG datasets to train machine learning models could help overcome the spatial limitations of bipolar electrodes. Unfortunately, differences in signal characteristics across acquisition systems prevent the direct transfer of models without a drop in performance. This study investigated Domain Adaptation (DA) to render EMG-based models invariant to different acquisition systems. This approach was evaluated using a Temporal Convolutional Network (TCN) that mapped EMG signals to the subject's knee angle, using HD-EMG as source data and Delsys bipolar EMG as target data. Furthermore, the feature extraction learnt by the TCN was also applied across muscle groups, evaluating the transferability of the sensor agnostic features. The DA implementation shows promise in both scenarios, with an average increase in accuracy (angular error normalised by the range of motion) of 7.36% for the Rectus Femoris, Biceps Femoris and Tibialis Anterior, as well as a cross-muscle performance increase of up to 10.80%. However, when the domain discrepancy is severe, the model is currently unable to generate a reliable walking trajectory due to inherent limitations related to the applied regression scheme and the chosen Mean Squared Error loss function. Therefore, future research should focus on exploring advanced loss functions and classification-based DA models that prioritise restoring key features of the gait.

3D muscle networks based on vibrational mechanomyography.

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials.

Introduction: Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods: This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson's disease patients. Results: Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion: Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study's findings can inform future design iterations of neural decoders for adaptive DBS and BCI.

Assessing a Sleep Interviewing Chatbot to Improve Subjective and Objective Sleep: Protocol for an Observational Feasibility Study.

BACKGROUND: Sleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases. OBJECTIVE: This work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants' self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep. METHODS: We will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrupted sleep. We will analyze questionnaire responses before and after the study to assess their perception of our proposed chatbot; questionnaire responses before, during, and after the study to assess their subjective sleep quality changes; and sleep parameters recorded by the sleep analyzer throughout the study to assess their objective sleep quality changes. RESULTS: Recruitment will begin in May 2023 through UK Dementia Research Institute Care Research and Technology Centre organized community outreach. Data collection will run from May 2023 until December 2023. We hypothesize that participants will perceive our proposed chatbot as intelligent and trustworthy; we also hypothesize that our proposed chatbot can help improve participants' subjective and objective sleep assessment throughout the study. CONCLUSIONS: The MotivSleep automated chatbot has the potential to provide additional care to older adults who wish to improve their sleep in more accessible and less costly ways than conventional face-to-face therapy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45752.

Caveolin-3 and Caveolae regulate ventricular repolarization.

RATIONALE: Flask-shaped invaginations of the cardiomyocyte sarcolemma called caveolae require the structural protein caveolin-3 (Cav-3) and host a variety of ion channels, transporters, and signaling molecules. Reduced Cav-3 expression has been reported in models of heart failure, and variants in CAV3 have been associated with the inherited long-QT arrhythmia syndrome. Yet, it remains unclear whether alterations in Cav-3 levels alone are sufficient to drive aberrant repolarization and increased arrhythmia risk. OBJECTIVE: To determine the impact of cardiac-specific Cav-3 ablation on the electrophysiological properties of the adult mouse heart. METHODS AND RESULTS: Cardiac-specific, inducible Cav3 homozygous knockout (Cav-3KO) mice demonstrated a marked reduction in Cav-3 expression by Western blot and loss of caveolae by electron microscopy. However, there was no change in macroscopic cardiac structure or contractile function. The QTc interval was increased in Cav-3KO mice, and there was an increased propensity for ventricular arrhythmias. Ventricular myocytes isolated from Cav-3KO mice exhibited a prolonged action potential duration (APD) that was due to reductions in outward potassium currents (Ito, Iss) and changes in inward currents including slowed inactivation of ICa,L and increased INa,L. Mathematical modeling demonstrated that the changes in the studied ionic currents were adequate to explain the prolongation of the mouse ventricular action potential. Results from human iPSC-derived cardiomyocytes showed that shRNA knockdown of Cav-3 similarly prolonged APD. CONCLUSION: We demonstrate that Cav-3 and caveolae regulate cardiac repolarization and arrhythmia risk via the integrated modulation of multiple ionic currents.

Inactivation of SARS-CoV-2 and Other Human Coronaviruses Aided by Photocatalytic One-Dimensional Titania Nanotube Films as a Self-Disinfecting Surface.

SARS-CoV-2 and its variants that continue to emerge have necessitated the implementation of effective disinfection strategies. Developing self-disinfecting surfaces can be a potential route for reducing fomite transmissions of infectious viruses. We show the effectiveness of TiO2 nanotubes (T_NTs) on photocatalytic inactivation of human coronavirus, HCoV-OC43, as well as SARS-CoV-2. T_NTs were synthesized by the anodization process, and their impact on photocatalytic inactivation was evaluated by the detection of residual viral genome copies (quantitative real-time quantitative reverse transcription polymerase chain reaction) and infectious viruses (infectivity assays). T_NTs with different structural morphologies, wall thicknesses, diameters, and lengths were prepared by varying the time and applied potential during anodization. The virucidal efficacy was tested under different UV-C exposure times to understand the photocatalytic reaction's kinetics. We showed that the T_NT presence boosts the inactivation process and demonstrated complete inactivation of SARS-CoV-2 as well as HCoV-OC43 within 30 s of UV-C illumination. The remarkable cyclic stability of these T_NTs was revealed through a reusability experiment. The spectroscopic and electrochemical analyses have been reported to correlate and quantify the effects of the physical features of T_NT with photoactivity. We anticipate that the proposed one-dimensional T_NT will be applicable for studying the surface inactivation of other coronaviruses including SARS-CoV-2 variants due to similarities in their genomic structure.

Accurate and Robust Locomotion Mode Recognition Using High-Density EMG Recordings from a Single Muscle Group.

Existing methods for human locomotion mode recognition often rely on using multiple bipolar electrode sensors on multiple muscle groups to accurately identify underlying motor activities. To avoid this complex setup and facilitate the translation of this technology, we introduce a single grid of high-density surface electromyography (HDsEMG) electrodes mounted on a single location (above the rectus femoris) to classify six locomotion modes in human walking. By employing a neural network, the trained model achieved average recognition accuracy of 97.7% with 160ms latency, significantly better than the model trained with one bipolar electrode pair placed on the same muscle (71.4% accuracy). To further exploit the spatial and temporal information of HDsEMG, we applied data augmentation to generate artificial data from simulated displaced electrodes, aiming to counteract the influence of electrode shifts. By employing a convolutional neural network with the enhanced dataset, the updated model was not strongly affected by electrode misplacement (93.9% accuracy) while models trained by bipolar electrode data were significantly disrupted by electrode shifts (29.4% accuracy). Findings suggest HDsEMG could be a valuable resource for mapping gait with fewer sensor locations and greater robustness. Results offer future promise for real-time control of assistive technology such as exoskeletons.

Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review.

The study of brain-to-brain synchrony has a burgeoning application in the brain-computer interface (BCI) research, offering valuable insights into the neural underpinnings of interacting human brains using numerous neural recording technologies. The area allows exploring the commonality of brain dynamics by evaluating the neural synchronization among a group of people performing a specified task. The growing number of publications on brain-to-brain synchrony inspired the authors to conduct a systematic review using the PRISMA protocol so that future researchers can get a comprehensive understanding of the paradigms, methodologies, translational algorithms, and challenges in the area of brain-to-brain synchrony research. This review has gone through a systematic search with a specified search string and selected some articles based on pre-specified eligibility criteria. The findings from the review revealed that most of the articles have followed the social psychology paradigm, while 36% of the selected studies have an application in cognitive neuroscience. The most applied approach to determine neural connectivity is a coherence measure utilizing phase-locking value (PLV) in the EEG studies, followed by wavelet transform coherence (WTC) in all of the fNIRS studies. While most of the experiments have control experiments as a part of their setup, a small number implemented algorithmic control, and only one study had interventional or a stimulus-induced control experiment to limit spurious synchronization. Hence, to the best of the authors' knowledge, this systematic review solely contributes to critically evaluating the scopes and technological advances of brain-to-brain synchrony to allow this discipline to produce more effective research outcomes in the remote future.

Mapping Lower-Limb Prosthesis Load Distributions Using a Low-Cost Pressure Measurement System.

Background: In the UK 55,000 people live with a major limb amputation. The prosthetic socket is problematic for users in relation to comfort and acceptance of the prosthesis; and is associated with the development of cysts and sores. Objectives: We have developed a prototype low-cost system combining low-profile pressure sensitive sensors with an inertial measurement unit to assess loading distribution within prosthetic sockets. The objective of this study was to determine the ability of this prototype to assess in-socket loading profiles of a person with an amputation during walking, with a view to understanding socket design and fit. Methods: The device was evaluated on four transtibial participants of various age and activity levels. The pressure sensors were embedded in the subject's sockets and an inertial measurement unit was attached to the posterior side of the socket. Measurements were taken during level walking in a gait lab. Results: The sensors were able to dynamically collect data, informing loading profiles within the socket which were in line with expected distributions for patellar-tendon-bearing and total-surface-bearing sockets. The patellar tendon bearing subject displayed loading predominately at the patellar tendon, tibial and lateral gastrocnemius regions. The total-surface bearing subjects indicated even load distribution throughout the socket except in one participant who presented with a large socket-foot misalignment. Conclusions: The sensors provided objective data showing the pressure distributions inside the prosthetic socket. The sensors were able to measure the pressure in the socket with sufficient accuracy to distinguish pressure regions that matched expected loading patterns. The information may be useful to aid fitting of complex residual limbs and for those with reduced sensation in their residual limb, alongside the subjective feedback from prosthesis users.

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals.

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography.

Functional muscle network is a critical concept in describing functional synergistic muscle synchronization and functional connectivity needed for the execution of complex motor tasks. Muscle network is typically derived from decomposition of intermuscular coherence (IMC) at different frequency bands of multichannel electromyography (EMG) measurements, which potentially limits out-of-clinic applications. In this investigation, we introduce muscle network analysis to assess the functional coordination and functional connectivity of muscles based on mechanomyography (MMG). We focus on a targeted group of muscles vital for activities of daily living (ADLs) in the upper-limb. Functional muscle networks are evaluated for ten able-bodied participants and three upper-limb amputees. Muscle activity was acquired from a custom-made wearable armband of MMG sensors placed over four superficial muscles around the forearm (flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. Muscle connectivity analysis at multiple frequency bands shows significant topographical differences across gestures for low (i.e., 5 Hz) and high (i.e., 12 Hz) activation frequencies as well as observable network differences between amputee and non-amputee subjects. Results demonstrate MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic functional synchronization of upper-limb muscles in complex movement tasks. The new physiological modality provides key insights into neural circuitry of motor coordination. Findings further offer the concomitant outcomes of demonstrating feasibility of MMG to map muscle coherence from a neurophysiological perspective and providing a mechanistic basis for its translation in human-robot interface.

Acceptability of Social Robots and Adaptation of Hybrid-Face Robot for Dementia Care in India: A Qualitative Study.

OBJECTIVES: This study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India. METHODS: We conducted a focus group discussion and in-depth interviews with persons with dementia (PwD), their caregivers, professionals in the field of dementia, and technical experts in robotics to collect qualitative data. RESULTS: This study explored the following themes: Acceptability of Robots in Dementia Care in India, Adaptation of Hybrid-Face Robot and Future of Robots in Dementia Care. Caregivers and PwD were open to the idea of social robot use in dementia care; caregivers perceived it to help with the challenges of caregiving and positively viewed a future with robots. DISCUSSION: This study is the first of its kind to explore the use of social robots in dementia care in India by highlighting user needs and requirements that determine acceptability and guiding adaptation.

MarketBrain: An EEG based intelligent consumer preference prediction system.

The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signal processing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (δ, θ, α, ß1, ß2, γ) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01±0.71) using 5-fold cross-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.

Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance.

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, 'Corner', has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation.

Quantification of Motor Function Post-Stroke Using Novel Combination of Wearable Inertial and Mechanomyographic Sensors.

Subjective clinical rating scales represent the gold-standard for diagnosis of motor function following stroke. In practice however, they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have not significantly impacted clinical practice. We address translational challenges in this arena through: (1) implementation of a novel sensor suite combining inertial measurement and mechanomyography (MMG) to quantify hand and wrist motor function; and (2) introduction of a new range of signal features extracted from the suite to supplement predicted clinical scores. The wearable sensors, signal features, and machine learning algorithms have been combined to produce classified ratings from the Fugl-Meyer clinical assessment rating scale. Furthermore, we have designed the system to augment clinical rating with several sensor-derived supplementary features encompassing critical aspects of motor dysfunction (e.g. joint angle, muscle activity, etc.). Performance is validated through a large-scale study on a post-stroke cohort of 64 patients. Fugl-Meyer Assessment tasks were classified with 75% accuracy for gross motor tasks and 62% for hand/wrist motor tasks. Of greater import, supplementary features demonstrated concurrent validity with Fugl-Meyer ratings, evidencing their utility as new measures of motor function suited to automated assessment. Finally, the supplementary features also provide continuous measures of sub-components of motor function, offering the potential to complement low accuracy but well-validated clinical rating scales when high-quality motor outcome measures are required. We believe this work provides a basis for widespread clinical adoption of inertial-MMG sensor use for post-stroke clinical motor assessment.

Robotic Telemedicine for Mental Health: A Multimodal Approach to Improve Human-Robot Engagement.

COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.e., trust) demands seamless affective human-robot interactions; natural 'human-like' conversations are required in simple, inexpensive, deployable platforms. We present the design, development, and pilot testing of a multimodal robotic framework fusing verbal (contextual speech) and nonverbal (facial expressions) social cues, aimed to improve engagement in human-robot interaction and ultimately facilitate mental health telemedicine during and beyond the COVID-19 pandemic. We report the design optimization of a hybrid face robot, which combines digital facial expressions based on mathematical affect space mapping with static 3D facial features. We further introduce a contextual virtual assistant with integrated cloud-based AI coupled to the robot's facial representation of emotions, such that the robot adapts its emotional response to users' speech in real-time. Experiments with healthy participants demonstrate emotion recognition exceeding 90% for happy, tired, sad, angry, surprised and stern/disgusted robotic emotions. When separated, stern and disgusted are occasionally transposed (70%+ accuracy overall) but are easily distinguishable from other emotions. A qualitative user experience analysis indicates overall enthusiastic and engaging reception to human-robot multimodal interaction with the new framework. The robot has been modified to enable clinical telemedicine for cognitive engagement with older adults and people with dementia (PwD) in LMICs. The mechanically simple and low-cost social robot has been deployed in pilot tests to support older individuals and PwD at the Schizophrenia Research Foundation (SCARF) in Chennai, India. A procedure for deployment addressing challenges in cultural acceptance, end-user acclimatization and resource allocation is further introduced. Results indicate strong promise to stimulate human-robot psychosocial interaction through the hybrid-face robotic system. Future work is targeting deployment for telemedicine to mitigate the mental health impact of COVID-19 on older adults and PwD in both LMICs and higher income regions.

Boosting Photocatalytic Activity Using Reduced Graphene Oxide (RGO)/Semiconductor Nanocomposites: Issues and Future Scope.

Semiconductor nanoparticles are promising materials for light-driven processes such as solar-fuel generation, photocatalytic pollutant remediation, and solar-to-electricity conversion. Effective application of these materials alongside light can assist in reducing the dependence on fossil-fuel driven processes and aid in resolving critical environmental issues. However, severe recombination of the photogenerated charge-carriers is a persistent bottleneck in several semiconductors, particularly those that contain multiple cations. This issue typically manifests in the form of reduced lifetime of the photoexcited electrons-holes leading to a decrease in the quantum efficiency of various light-driven applications. On the other hand, semiconducting oxides or sulfides, coupled with reduced graphene oxide (RGO), have drawn a considerable interest recently, partly because of the RGO enhancing charge separation and transportation through its honeycomb sp2 network structure. High electron mobility, conductivity, surface area, and cost-effectiveness are the hallmark of the RGO. This Mini-Review focuses on (1) examining the approach to the integration of RGO with semiconductors to produce binary nanocomposites; (2) insights into the microstructure interface, which plays a critical role in leveraging charge transport; (3) key examples of RGO composites with oxide and sulfide semiconductors with photocatalysis as application; and (4) strategies that have to be pursued to fully leverage the benefit of RGO in RGO/semiconductors to attain high photocatalytic activity for a sustainable future. This Mini-Review focuses on areas requiring additional exploration to fully understand the interfacial science of RGO and semiconductor, for clarity regarding the interfacial stability between RGO and the semiconductor, electronic coupling at the heterojunction, and morphological properties of the nanocomposites. We believe that this Mini-Review will assist with streamlining new directions toward the fabrication of RGO/semiconductor nanocomposites with higher photocatalytic activity for solar-driven multifunctional applications.