HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses.

Neurotechnology and Artificial Intelligence (AI) have developed rapidly in recent years with an increasing number of applications and AI-enabled devices that are about to enter the market. While promising to substantially improve quality of life across various severe medical conditions, there are also concerns that the convergence of these technologies, e.g., in the form of intelligent neuroprostheses, may have undesirable consequences and compromise cognitive liberty, mental integrity, or mental privacy. Therefore, various international organizations, such as the Organization for Economic Cooperation and Development (OECD) or United Nations Educational, Scientific and Cultural Organization (UNESCO), have formed initiatives to tackle such questions and develop recommendations that mitigate risks while fostering innovation. In this context, a first international conference on the ethics and regulation of intelligent neuroprostheses was held in Berlin, Germany, in autumn 2023. The conference gathered leading experts in neuroscience, engineering, ethics, law, philosophy as well as representatives of industry, policy making and the media. Here, we summarize the highlights of the conference, underline the areas in which a broad consensus was found among participants, and provide an outlook on future challenges in development, deployment, and regulation of intelligent neuroprostheses.

Affordance trajectories and the usefulness of online records access among older adults in Sweden.

Objective: The current understanding of the breadth of individual differences in how eHealth technologies are perceived as useful for different purposes is incomprehensive. The aim/purpose of the study is to improve the understanding of diverse perceptions of the usefulness of technologies by exploring older adults' use of their patient-accessible electronic health records (PAEHRs). Methods: The study applies and extends Affordance Theory based on an empirical analysis of data from the NORDeHEALTH 2022 Patient Survey on attitudes toward PAEHR in Norway, Sweden, Finland, and Estonia. Responses from 3964 participants in Sweden, aged 65 + years were analysed. Data included demographics and agreement ratings to reasons for using PAEHR. To analyse variation in the reasons for using PAEHR, group comparisons were conducted based on gender (male/female), age group (65-74, 75-84 and 85+) and earlier encouragement to use PAEHR. Results: Overall, the findings suggest that PAEHRs have multiple parallel affordance trajectories and affordance potencies that actualise differently depending on needs. The top reasons, pointing to both orientational and goal-oriented affordances for using PAEHR, were improving understanding of health issues, getting an overview of medical history/treatment and ensuring understanding of what the doctor said. Men reported more often sharing information with relatives or friends as a reason to access PAEHR. Women were more inclined, albeit similarly to men less frequently, to read their PAEHR for detecting errors. Age had little influence on reasons for using PAEHR. Conclusions: The study applies and extends Affordance Theory in the context of older adults' PAEHR use based on findings from the largest national investigation of reasons for older users to access PAEHR in Sweden demonstrating the applicability of the theory in improving the understanding of the diversity of individual perceptions on eHealth technologies.

A service failure assessment model for smart product consumption experience based on customer perception.

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers' behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Design of a Capacitive Tactile Sensor Array System for Human-Computer Interaction.

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from "0" to "9". These results show the potential of the capacitive sensor array for advanced human-computer interaction applications.

Multimodal human-computer interaction in interventional radiology and surgery: a systematic literature review.

PURPOSE: As technology advances, more research dedicated to medical interactive systems emphasizes the integration of touchless and multimodal interaction (MMI). Particularly in surgical and interventional settings, this approach is advantageous because it maintains sterility and promotes a natural interaction. Past reviews have focused on investigating MMI in terms of technology and interaction with robots. However, none has put particular emphasis on analyzing these kind of interactions for surgical and interventional scenarios. METHODS: Two databases were included in the query to search for relevant publications within the past 10 years. After identification, two screening steps followed which included eligibility criteria. A forward/backward search was added to identify more relevant publications. The analysis incorporated the clustering of references in terms of addressed medical field, input and output modalities, and challenges regarding the development and evaluation. RESULTS: A sample of 31 references was obtained (16 journal articles, 15 conference papers). MMI was predominantly developed for laparoscopy and radiology and interaction with image viewers. The majority implemented two input modalities, with voice-hand interaction being the most common combination-voice for discrete and hand for continuous navigation tasks. The application of gaze, body, and facial control is minimal, primarily because of ergonomic concerns. Feedback was included in 81% publications, of which visual cues were most often applied. CONCLUSION: This work systematically reviews MMI for surgical and interventional scenarios over the past decade. In future research endeavors, we propose an enhanced focus on conducting in-depth analyses of the considered use cases and the application of standardized evaluation methods. Moreover, insights from various sectors, including but not limited to the gaming sector, should be exploited.

Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study.

BACKGROUND: Suicide stands as a global public health concern with a pronounced impact, especially in low- and middle-income countries, where it remains largely unnoticed as a significant health concern, leading to delays in diagnosis and intervention. South Asia, in particular, has seen limited development in this area of research, and applying existing models from other regions is challenging due to cost constraints and the region's distinct linguistics and behavior. Social media analysis, notably on platforms such as Facebook (Meta Platforms Inc), offers the potential for detecting major depressive disorder and aiding individuals at risk of suicidal ideation. OBJECTIVE: This study primarily focuses on India and Bangladesh, both South Asian countries. It aims to construct a predictive model for suicidal ideation by incorporating unique, unexplored features along with masked content from both public and private Facebook profiles. Moreover, the research aims to fill the existing research gap by addressing the distinct challenges posed by South Asia's unique behavioral patterns, socioeconomic conditions, and linguistic nuances. Ultimately, this research strives to enhance suicide prevention efforts in the region by offering a cost-effective solution. METHODS: This quantitative research study will gather data through a web-based platform. Initially, participants will be asked a few demographic questions and to complete the 9-item Patient Health Questionnaire assessment. Eligible participants who provide consent will receive an email requesting them to upload a ZIP file of their Facebook data. The study will begin by determining whether Facebook is the primary application for the participants based on their active hours and Facebook use duration. Subsequently, the predictive model will incorporate a wide range of previously unexplored variables, including anonymous postings, and textual analysis features, such as captions, biographic information, group membership, preferred pages, interactions with advertisement content, and search history. The model will also analyze the use of emojis and the types of games participants engage with on Facebook. RESULTS: The study obtained approval from the scientific review committee on October 2, 2023, and subsequently received institutional review committee ethical clearance on December 8, 2023. Our system is anticipated to automatically detect posts related to depression by analyzing the text and use pattern of the individual with the best accuracy possible. Ultimately, our research aims to have practical utility in identifying individuals who may be at risk of depression or in need of mental health support. CONCLUSIONS: This initiative aims to enhance engagement in suicidal ideation medical care in South Asia to improve health outcomes. It is set to be the first study to consider predicting participants' primary social application use before analyzing their content to forecast behavior and mental states. The study holds the potential to revolutionize strategies and offer insights for scalable, accessible interventions while maintaining quality through comprehensive Facebook feature analysis. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55511.

Spray-Coated Ultrathin and Porous Films for Physiological Sensing and Force Detection.

Epidermal electronics employed on human skin for the long term require good breathability and nonforeign wearing. In this work, we combine phase separation and spray coating to fabricate a porous and ultrathin electrode within minutes as well as micrometer-scale porous pressure sensors. The resulting electrodes show a water vapor transmission rate of 18.4 mg·cm-2·h-1, sheet resistance of 5.2 Ω/sq, and thickness below 5 µm. The introduction of the biogel further reduced the electrode-skin impedance, which is lower than that of the commercial gel electrode, indicating that the electrode can have a high degree of conformal contact with the skin. The epidermal electronics prepared by this strategy exhibit an excellent performance in force sensing. Such results strongly prove the efficiency and practicality of the strategy.

Research perspectives and trends in Artificial Intelligence-enhanced language education: A review.

Artificial Intelligence (AI) has had a significant impact on language education, arousing the related studies from different fields and perspectives. However, the extant review articles only focused on a certain aspect in the field, thus neglecting the multi-disciplinary and multi-perspective features of AI-enhanced language education. The aim of the current study is to bridge this gap by reviewing the 812 articles selected from Web of Science via CiteSpace. The study found that the existing studies were mainly conducted from the perspectives of computer science, linguistics and psychology. Besides, the individualized development of language education supported by technology was not obvious, and the degree of human-computer interaction was weak. Moreover, previous studies focused more on the advantages of AI-enhanced language education than the disadvantages, and the empirical research to analyze specific problems was insufficient. In the future, the research tends to strengthen the cooperation among related disciplines and focus on the individualized development of language education. In addition, empirical research should be strengthened to explore the efficiency of AI-enhanced language education in order to promote the reform of language education.

Effects of video game immersion and task interference on cognitive performance: a study on immediate and delayed recall and recognition accuracy.

This study investigates the cognitive impacts of video game immersion and task interference on immediate and delayed recall as well as recognition tasks. We enrolled 160 subjects aged 18 to 29, who were regular players of "shoot-em-up" video games for at least 3 years. Participants were assigned to one of three experimental groups or a control group. The experimental conditions varied in the timing and type of tasks: the first group performed a video game session between recall tasks, the second group multitasked with video games and recall tasks simultaneously, and the third group engaged in task switching from video games to recall tasks. Using the Rey Auditory Verbal Learning Test, we measured the effects of these conditions on cognitive performance, focusing on error types and recall accuracy. Results indicated that multitasking and task switching significantly affected the subjects' performance, with notable decrements in recall and recognition accuracy in conditions of high task interference. The study highlights the cognitive costs associated with multitasking in immersive digital games and provides insights into how task similarity and interference might increase error rates and affect memory performance.

Speech recognition technology for assessing team debriefing communication and interaction patterns: An algorithmic toolkit for healthcare simulation educators.

BACKGROUND: Debriefings are central to effective learning in simulation-based medical education. However, educators often face challenges when conducting debriefings, which are further compounded by the lack of empirically derived knowledge on optimal debriefing processes. The goal of this study was to explore the technical feasibility of audio-based speaker diarization for automatically, objectively, and reliably measuring debriefing interaction patterns among debriefers and participants. Additionally, it aimed to investigate the ability to automatically create statistical analyses and visualizations, such as sociograms, solely from the audio recordings of debriefings among debriefers and participants. METHODS: We used a microphone to record the audio of debriefings conducted during simulation-based team training with third-year medical students. The debriefings were led by two healthcare simulation instructors. We processed the recorded audio file using speaker diarization machine learning algorithms and validated the results manually to showcase its accuracy. We selected two debriefings to compare the speaker diarization results between different sessions, aiming to demonstrate similarities and differences in interaction patterns. RESULTS: Ten debriefings were analyzed, each lasting about 30 min. After data processing, the recorded data enabled speaker diarization, which in turn facilitated the automatic creation of visualized interaction patterns, such as sociograms. The findings and data visualizations demonstrated the technical feasibility of implementing audio-based visualizations of interaction patterns, with an average accuracy of 97.78%.We further analyzed two different debriefing cases to uncover similarities and differences between the sessions. By quantifying the response rate from participants, we were able to determine and quantify the level of interaction patterns triggered by instructors in each debriefing session. In one session, the debriefers triggered 28% of the feedback from students, while in the other session, this percentage increased to 36%. CONCLUSION: Our results indicate that speaker diarization technology can be applied accurately and automatically to provide visualizations of debriefing interactions. This application can be beneficial for the development of simulation educator faculty. These visualizations can support instructors in facilitating and assessing debriefing sessions, ultimately enhancing learning outcomes in simulation-based healthcare education.

Advances in the development and application of non-contact intraoperative image access systems.

This article provides an overview of recent progress in the achievement of non-contact intraoperative image control through the use of vision and sensor technologies in operating room (OR) environments. A discussion of approaches to improving and optimizing associated technologies is also provided, together with a survey of important challenges and directions for future development aimed at improving the use of non-contact intraoperative image access systems.

Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation.

Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.

Facilitators of and Barriers to Teachers' Engagement With Consumer Technologies for Stress Management: Qualitative Study.

BACKGROUND: Consumer technology is increasingly being adopted to support personal stress management, including by teachers. Multidisciplinary research has contributed some knowledge of design and features that can help detect and manage workplace stress. However, there is less understanding of what facilitates engagement with ubiquitous "off the shelf" technologies, particularly in a specific occupational setting. An understanding of features that facilitate or inhibit technology use, and the influences of contexts on the manner of interaction, could improve teachers' stress-management opportunities. OBJECTIVE: The aim of the study was to investigate the interaction features that facilitated or inhibited engagement with 4 consumer technologies chosen by teachers for stress management, as well as the influence of the educational contexts on their engagement. We also examined how use of well-being technology could be better supported in the school. METHODS: The choice of consumer technologies was categorized in a taxonomy for English secondary school teachers according to stress-management strategies and digital features. Due to the COVID-19 pandemic, we adapted the study so that working from home in the summer could be contrasted with being back in school. Thus, a longitudinal study intended for 6 weeks in the summer term (in 2020) was extended into the autumn term, lasting up to 27 weeks. Teachers chose to use either a Withings smartwatch or Wysa, Daylio, or Teacher Tapp apps. Two semistructured interviews and web-based surveys were conducted with 8 teachers in England in the summer term, and 6 (75%) of them took part in a third interview in the autumn term. Interviews were analyzed using reflexive thematic analysis informed by interpretive phenomenological analysis. RESULTS: Technology elements and characteristics such as passive data collation, brevity of interaction, discreet appearance, reminders, and data visualization were described by teachers as facilitators. Lack of instructions and information on features, connectivity, extended interaction requirements, and nondifferentiation of activity and exercise data were described as barriers. Mesocontextual barriers to engagement were also reported, particularly when teachers were back on school premises, including temporal constraints, social stigma, and lack of private space to de-stress. Teachers had ideas for feature improvements and how educational leadership normalizing teachers' stress management with consumer technologies could benefit the school culture. CONCLUSIONS: Having preselected their stress-management strategies, teachers were able to harness design features to support themselves over an extended period. There could be an important role for digital interventions as part of teachers' stress management, which the school leadership would need to leverage to maximize their potential. The findings add to the holistic understanding of situated self-care and should inform developers' considerations for occupational digital stress support.

Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences.

Objective: Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience. Methods: We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted. Results: The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users. Conclusion: The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants' subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Digitalizing Handwritten Digits of Patients with Parkinson's Disease Utilizing Consumer Hardware and Open-Source Software.

INTRODUCTION: Parkinson's disease represents a burdensome condition with complex manifestations. A licensed, standardized paper-based questionnaire is completed by both patients and physicians to monitor the progression and state of the disease. However, integrating the obtained scores into digital systems still poses a challenge. METHODS: Paper-based handwriting is intuitive and an efficient mode of human-computer interaction. Accordingly, we transformed a consumer-grade tablet into a device where an exact digital copy of the disease-specific questionnaire can be filled with the supplied pen. Utilizing a small convolutional neural network directly on the device and trained on MNIST data, we translated the handwritten digits to appropriate LOINC codes and made them accessible through a FHIR-compatible HTTP interface. RESULTS: When evaluating the usability from a patient-centric point of view, the System Usability Score revealed an excellent rating (SUS = 83.01) from the participants. However, we identified some challenges associated with the magnetic pen and the flat design of the device. CONCLUSION: In setups where certified medical devices are not required, consumer hardware can be used to map handwritten digits of patients to appropriate medical standards without manual intervention through healthcare professionals.

Telerobotic Intergroup Contact: Acceptance and Preferences in Israel and Palestine.

We explore telerobotics as a novel form of intergroup communication. In this form, remotely operated robots facilitate embodied and situated intergroup contact between groups in conflict over long distances, potentially reducing prejudice and promoting positive social change. Based on previous conceptual frameworks and design hypotheses, we conducted a survey on the acceptance and preferences of the telerobotic medium in Israel and Palestine. We analyzed the responses using a mixed-method approach. The results shed light on differences in attitudes between the groups and design considerations for telerobots when used for intergroup contact. This study serves as a foundation for the implementation of a novel method of technology-enhanced conflict resolution in the field.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study.

BACKGROUND: Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. OBJECTIVE: We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. METHODS: We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. RESULTS: We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=-5.49, P<.001, Cohen d=0.36). CONCLUSIONS: Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array.

Continuous and reliable monitoring of gait is crucial for health monitoring, such as postoperative recovery of bone joint surgery and early diagnosis of disease. However, existing gait analysis systems often suffer from large volumes and the requirement of special space for setting motion capture systems, limiting their application in daily life. Here, we develop an intelligent gait monitoring and analysis prediction system based on flexible piezoelectric sensors and deep learning neural networks with high sensitivity (241.29 mV/N), quick response (66 ms loading, 87 ms recovery), and excellent stability (R2 = 0.9946). The theoretical simulations and experiments confirm that the sensor provides exceptional signal feedback, which can easily acquire accurate gait data when fitted to shoe soles. By integrating high-quality gait data with a custom-built deep learning model, the system can detect and infer human motion states in real time (the recognition accuracy reaches 94.7%). To further validate the sensor's application in real life, we constructed a flexible wearable recognition system with human-computer interaction interface and a simple operation process for long-term and continuous tracking of athletes' gait, potentially aiding personalized health management, early detection of disease, and remote medical care.