Falls due to balance impairment are a leading cause of injury among older adults, yet routine medical diagnostics often overlook balance assessment. To address this gap, we developed a low-cost, home-based balance board system that combines physical hardware with gamified software to assess and potentially improve users' balance through interactive tasks. Our system integrates load cell sensors with a dedicated computer to track shifts in the user's center of pressure (COP) as they play balanceoriented games. Data from these sessions are automatically visualized and uploaded to a cloud-based dashboard for both users and clinicians. A feasibility study with 10 healthy adult participants was conducted, with results supporting full system functionality and user satisfaction, as well as improved performance across trials. The findings in this paper suggest that the system is not only feasible but could also increase adherence to balance exercises for training and facilitate broader accessibility to balance assessments outside clinical settings.

Electrodermal activity (EDA) provides a direct indicator of sympathetic nervous system arousal through changes in skin conductance. However, wearable EDA sensing poses challenges such as inconsistent skin contact, electrode impedance variability, motion artifacts, and power constraints. To address these issues, this study presents mobile EDA (mEDA), a compact device driven by a stabilized direct-current source. A validation study was conducted on ten healthy adult participants in a time-synchronized protocol to collect data from BIOPAC and mEDA concurrently. mEDA recordings employed gel electrodes for P1-P5 and dry (textile) electrodes for P6-P10, while the BIOPAC MP160 system used gel electrodes for all participants. Participants underwent a 30-minute protocol of resting, deep breathing, and three cognitive tasks. The preprocessing pipeline consisted of low-pass filter and artifact (sharp peaks and flat line) removal. Cleaned signals were converted into frequency domain components for decomposition into low and high frequency components, skin conductance level (SCL), and skin conductance response (SCR) respectively. SCL and SCR were converted back to the time domain to analyze performance metrics between both devices. Pearson correlation, coherence, and Dynamic Time Warping (DTW) were computed on SCL, while zero-crossing peaks were counted for SCR analysis. With gel electrodes, the average Pearson correlation was 0.92 and the SCR peak count difference was 38. For textile electrodes, the correlation was 0.88 with a peak count difference of 119. Both configurations achieved coherence above 0.95 and DTW below 0.5 for most participants. These results demonstrate mEDA's reliable performance in capturing both tonic and phasic EDA across electrode configurations.

In-sensor computing (ISC) represents a paradigm shift in sensor integration technologies. ISC system combines sensing and processing elements on a single chip, enabling real-time processing and reducing computation latency by eliminating massive data transfer and analog-to-digital conversion. However, due to the high integration of sensors and computation units, ISC could suffer from new security attacks that have not been explored yet. In this work, we investigate a covert channel attack that leverages the analog nature of ISC to showcase the feasibility of security attacks and severe consequences in the context of real applications. We envision that this research paper will inspire more researchers to brainstorm new defense mechanisms for the emerging ISC.

This manuscript investigates behavioral differences between children with ADHD and neurotypical children (6-11y) using smartwatch data from both hands collected during four classroom-like activities. Our study analyzes accelerometer and gyroscope data to identify distinct behavioral patterns, that can be used to enhance diagnostic methods for ADHD. We pre-processed the smartwatch data and calculated statistical features such as mean, median, standard deviation, kurtosis, and sum of consecutive differences. Wrist angles were used to differentiate on-task and off-task states. Hypothesis testing indicated that certain activities and features, such as the calculated angle of the y-axis in activity 1 (magnetic tiles) and the 'sum of consecutive differences' in activity 4 (drawing), were significant in distinguishing between the two groups. The left hand (non-dominant hand) generally provided more discriminative data than the right hand (dominant hand). Our findings suggest that specific patterns in smartwatch data can differentiate between ADHD and neurotypical children during classroom activities. These insights hold promise for developing more objective and data-driven diagnostic tools for ADHD.

Attention Deficit Hyperactive Disorder (ADHD) is a neurodevelopmental condition. Globally, more than 366 million individuals are diagnosed with ADHD. ADHD is a chronic condition and has no known cure. However, treatments do exist in the form of behavior therapy and medication. Currently, management of ADHD symptoms during treatment is done through standardized questionnaire assessments. While valuable, these assessments are subjective. Researchers have proposed using computer-based games for objective monitoring of ADHD symptoms. In this work, we present MindGame, a puzzle game designed to monitor ADHD symptoms using computer mouse movement. To evaluate the feasibility of this application, we conducted an in-home study (N=4; 2 Neurotypical and 2 ADHD) participants who were asked to play the game 8–10 times in one- week at-home. Computer mouse data from 128 individual puzzle levels were analyzed. The game has three levels built-in with increasing level of difficulty. A significant difference (p-value=0.01786) between ADHD and Neurotypical participants in the Euclidean distance of the mouse cursor movement. All participants completed the study at-home 8–10 times except for P2 who was found dropped out of the study due to personal reasons.

One-fifth of adults in United States take at least five daily prescription medications. Maintaining a consistent medication schedule is particularly challenging for individuals with neurodegenerative disorders due to declines in cognitive and motor skills. For example, individuals with Parkinson's disease experience impaired fine motor skills and tremors, which can lead to difficulty in handling small pills or opening standard medication bottles. Additionally, the complexity of medication regimens in these patients further complicates timely and accurate medication intake management. Using a medication dispenser can help patients maintain independence in management thereby reducing caregiver burden. In this work, we introduce MedDock, a medication management system featuring a 3D-printed smart cap and a textile-based force-resistive sensor designed to automatically dispense medication at pre-set times without the users need to open the bottle cap. The MedDock device is equipped with an ESP8266 Wi-Fi Microcontroller Unit and servo motors that control the pill dispensing mechanism at regular intervals. The textile sensor is used to detect pill drop (i.e., when the pill is dispensed) and pill pick up (i.e., when the pill is taken by the user). To test the feasibility of our system we conducted an in-home experiment (N=8 healthy participants; 4 female, 4 male; 54–85 years old). The system was deployed for 3 days to the participant's home. To analyze the feasibility, we evaluated the time elapsed between pill drop and pill intake which was found to be on average of 15.97 (±7.53) seconds. We present the design, comprehensive benchtop testing, and results from an in-home deployment study, demonstrating the feasibility and effectiveness of MedDock in supporting and monitoring medication adherence.

A neonatal intensive care unit (NICU) provides an optimal environment for the care of preterm babies. Bedside nurses are fundamental to this care provided to preterm babies in the NICU. Modern NICUs are technology-intensive space, instrumented with several monitoring technologies to help the nurses track the babies in their care. These technologies help the nurses in a way that is essential for the successful operation of the NICU. To understand how these monitoring technologies function in the NICU from the viewpoint of the nurses, we conducted semi-structured interviews with seven nurses who work at a NICU in the US. We then performed a thematic analysis on the interviews and we found that, despite the utility of the monitoring technologies, they also pose several challenges to the nurses in performing their duties. More specifically, we discovered that: (1) all elements of the monitoring technologies posed a challenge in some way; (2) in a few specific situations, the nurses were able to make up for some of these challenges; and (3) the nurses suggested improvements to all elements of the monitoring technologies. Based on these findings, we describe six areas of future research that argue for the design of monitoring technologies as a way to empower nurses. These include: improved vital signs monitoring that facilitate kangaroo care, using voice to manage alarms, video feeds controlled by nurses in the patient rooms, giving more control over the alarm sounds to the nurses, having a common interface and leveraging augmented reality to help the nurses control the monitoring technologies.

An increasing number of people are being diagnosed with neurodegenerative diseases every year. One of the aims of digital health is to provide innovative solutions for rehabilitation or pre-diagnosis of these diseases. In this vein, e-textiles can allow for the construction of flexible and comfortable devices to record biosignals and develop novel rehabilitation platforms. In this study, an e-textile forearm band was constructed to acquire an electromyogram (EMG: a muscle response to a nerve’s stimulation of the muscle) from the flexor muscles of the hand to detect hand grips/squeezes. A light and portable data acquisition module, as well as a computer-based virtual reality game (controlled by the acquired EMG signal) were designed. Our pilot study showed that the system is reliable in detecting hand squeezes in both 6 healthy participants (F-score: 0.88) and 6 participants with Parkinson’s disease (F-score: 0.88). Participants also rated the system to be enjoyable, comfortable, and easy to use.

The field of wearable sensors has witnessed remarkable progress in recent years, enabling real-time monitoring of musculoskeletal biomechanics. Musculoskeletal injuries require long-term management through medications and physical therapy to ensure a smooth recovery. Assessment of a patient’s range of motion (ROM) is an essential component of physical therapy. However, existing methods for measuring ROM are either analog or require expensive equipment. In this work, we present ElboSense, a smart textile-based sensing solution to monitor elbow motion precisely. ElboSense is embedded with a novel yarn-like capacitive strain sensor made with braided composite structures of multiple filament strands in a helical pattern. We seamlessly integrated the sensor into an elbow brace and developed an embedded system for data acquisition. The work conducted a study with eight healthy adult participants who performed a regulated elbow movement exercise (flexion and extension) 10 times. We validated the sensor measurements against a reliable optical motion-capture system. The average cross-correlation was found to be 0.91. Pearson’s correlation coefficient analysis showed 0.95 for peak-to-peak and 0.98 for valley-to-valley. All the preliminary results indicate a good agreement and reliability as a potential sensing solution to monitor the range of motion during elbow exercises.

Parkinson’s disease (PD) is a neurological disorder that nearly affects 1 million people in the US alone. PD causes involuntary motor fluctuations, resulting in bradykinesia (slowness of movement), tremors, stiffness, walking imbalance, and other complications. PD has no known cure; however, treatment exists through regular medication intake and therapy. Clinicians rate the progression of the symptoms based on standard clinical assessments during in-clinic visits that occur 2-3 times a year. In addition to self-reports from the patients, neurologists find it helpful if motor symptoms can be monitored objectively at in-home settings for better insights into disease progression. This work explores the impact of medication on motor symptoms, such as the spatial-temporal and frequency features of the finger-tapping motor exam. In our prior research, people with Parkinson’s Disease (n=4) were given to wear a pair of smart gloves that collected data from 156 sessions of the finger-tapping exam. To determine speed and amplitude in the finger tapping exam, we explored two different adaptive peak detection algorithms to detect peaks in the raw data. Human raters detected peaks to provide ground truth for analyzing the accuracy of the peak detectors. The average mean absolute error percentage for peak detection algorithms was 16%. We investigated the effect of medication wear off and our findings suggest a statistically significant difference in the dominant frequency and the peak-to-peak difference in time (p-value: 0.0256, 0.0366). A greater variability was also observed when medication effects were minimal. These findings are promising and encourage us to further investigate the smart gloves in a larger and longitudinal in-home study on Parkinson’s.

Breathing exercises are gaining attention in managing anxiety and stress in daily life. Diaphragmatic breathing, in particular, fosters tranquility for both body and mind. Existing methods, such as meditation, yoga, and medical devices for guided breathing, often require expert guidance, complex instruments, cumbersome devices, and sticky electrodes. To address these challenges, we present Nisshash, an IoT-based smart T-shirt offering a personalized solution for regulated breathing exercises. Nisshash is embedded with three-channel e-textile respiration sensors and a tailored analog front-end (AFE) board to simultaneously monitor respiration rate (RR) and heart rate (HR). In this work, we seamlessly integrate soft textile sensors into a T-shirt and develop a detachable and Wi-Fi-enabled (2.4GHz) bio-instrumentation board, creating a pervasive wireless system (WPS) for guided breathing exercises (GBE). The system features an intuitive graphical user interface (GUI) and a seamless IoT-based control and computing system (CCS). It offers real-time instructions for inhaling and exhaling at various breathing speeds, including slow, normal, and fast breathing. Functions such as filtering, peak detections for respiration, and heart rate analysis are computed conjointly at the sender and receiver ends. We utilized the Pan-Tompkins and custom algorithms to calculate HR and RR from the filtered time-series signals. We conducted a study with 10 healthy adult participants who wore the T-shirt and performed guided breathing exercises. The average respiration event (inhale-exhale) detection accuracy was ≈98%. We validated the recorded HR against the 3-lead standard ECG monitoring device, achieving an accuracy of ≈99%. The RR-HR correlation analysis showed an R square value of 0.987. Collectively, these results demonstrate Nisshash’s potential as a personal guided breathing exercise solution.

The future generation of the Internet of Things (IoT) systems is characterized by the fusion of technologies—from edge–fog–cloud computing to artificial intelligence (AI) and blockchain—closing the gap between the physical and digital worlds [A1]. Although these technologies have been developed separately over time, the synergy among them has taken a giant leap. We are witnessing a fast-paced convergence of these technologies resulting in a fundamental paradigm shift unlocking vast benefits and opportunities across vertical markets. However, there are still several barriers, such as a lack of consensus toward any reference models or best practices, hindering the full fusion of these technologies [A1]. To tackle these challenges and facilitate this promising transformation, this special issue was organized to provide a holistic multidisciplinary reference for solutions, architectures, protocols, services, and applications addressing all aspects of the future generation of IoT systems via the fusion of edge, cloud, AI, and blockchain, while considering the corresponding challenges. Thanks to the enormous support from the Editor-in-Chief, Prof. Honggang Wang, and the dedicated work of many reviewers, after a rigorous review process, 27 excellent articles out of 125 submissions were accepted for inclusion in this special issue of the IEEE Internet of Things Journal. We introduce these papers and highlight their key contributions below.

The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

A rising number of preterm babies demands innovative solutions to monitor them in the Neonatal Intensive Care Unit (NICU) continuously. NICU monitors various kinds of vital signs. Among them, there is a strong demand for an accurate and sophisticated technology to monitor respiration rate (RR) and detect critical events such as apnea. Existing solutions for RR monitoring either rely on the indirect measurements from thoracic impedance or other invasive techniques posing discomfort and risk of infections to babies. Also, multiple wire loops lying around babies hinder the delivery of parental and clinical care. Motivated by this need, we have designed an Internet-of-Things (IoT) based smart textile chest belt called "Baby-Guard" to monitor RR and detect apnea. The Baby-Guard is a neonatal wearable system consisting of a sensor belt, a wearable embedded system, and an edge computing device. The sensor belt consists of textile-based pressure sensors and an Inertial Measurement Unit (IMU). The wearable system consists of a microcontroller equipped with wireless connectivity and power management. The edge computing device (ECD) connects with the wearable system through an MQTT networking architecture. ECD hosts signal processing and computing services to extract RR and detect apnea. We conducted simulation experiments using a high-fidelity, programmable NICU baby mannequin. We found an average error of 0.89 BrPM in breathing rate and ~97 percent accuracy in apnea detection. Computation and communication latencies were found to be ~66 and 22 ms, respectively. The Baby-Guard showed potential to be a wireless infant monitoring system in the NICU settings.

Utilizing a consumer-grade smartwatch in conjunction with a prescribed exercise therapy plan can help to reduce the patient-level entry barriers into programs designed for patients with peripheral arterial disease, which affects millions of people worldwide. Currently, the alternative to this physical therapy plan is surgical therapy which costs between $3 and $5 billion annually. This paper presents the development and testing of WalkCoach app, a smart service system integrating a consumer-grade smartwatch (Polar M600) in the monitoring of supervised walking exercises. By monitoring a participant's baseline activity and improvements with time, it will be possible to provide personalized exercise prescriptions that can be easily modified or personalized to adjust and optimize for improved walking ability as the therapy progresses. This paper demonstrates the accuracy of the smartwatch-based WalkCoach app in a pilot cohort study of 10 healthy older adults (>65 yrs) who were recruited to perform a 400m overground walking task. Results are promising and show that the consumer-grade smartwatch accurately measures steps (step count = 637) compared to a video/manual step count (650 steps; Pearson's r = 0.96, P <;0.001). In the future, WalkCoach will be improved to produce granular analytics on a patient's compliance and performance to the supervised walking exercises.

With 40 million amputees living in poor countries with limited access to health care, there is a significant need for novel prosthetic solutions. This paper presents our preliminary results to develop sustainable prostheses for five upper-extremity amputee patients in Colombia, South America. We utilized a patient-centric methodology, engaging patients at every stage in an effort to increase their satisfaction with their prosthetic and to assess clinical usefulness. We identified patient-specific criteria and compared existing open source hardware systems using the Design Thinking methodology. Finally, we present these data to further the development of myoelectric prosthetics as part of cyber-physical systems.

There is an increasing demand for smart fog-computing gateways as the size of cloud data is growing. This paper presents a Fog computing interface (FIT) for processing clinical speech data. FIT builds upon our previous work on EchoWear, a wearable technology that validated the use of smartwatches for collecting clinical speech data from patients with Parkinson's disease (PD). The fog interface is a low-power embedded system that acts as a smart interface between the smartwatch and the cloud. It collects, stores, and processes the speech data before sending speech features to secure cloud storage. We developed and validated a working prototype of FIT that enabled remote processing of clinical speech data to get speech clinical features such as loudness, short-time energy, zero-crossing rate, and spectral centroid. We used speech data from six patients with PD in their homes for validating FIT. Our results showed the efficacy of FIT as a Fog interface to translate the clinical speech processing chain (CLIP) from a cloud-based backend to a fog-based smart gateway.

The concurrent popularity of wearable sensors and Internet-of-Things (IoT) brings significant benefits to body sensor networks (BSN) that could communicate with the cloud computing platforms for bringing interoperability in health and wellness monitoring. We designed Pulse-Glasses that are cloud-connected, wearable, smart eyeglasses for unobtrusive and continuous heart rate (HR) monitoring. We 3D-printed the first prototype of Pulse-Glasses that use a photoplethysmography (PPG) sensor on one of the nose-pads to collect HR data. We integrated other circuits including an embedded board with Bluetooth low energy (BLE) and a rechargeable battery inside the two temples of Pulse-Glasses. We implemented IoT functionalities such that HR data are recorded from Pulse-Glasses, visualized on an Android smartphone, and stored seamlessly on the cloud. In this paper, we present the developments of Pulse-Glasses hardware including IoT services and the preliminary results from validation experiments. We compared Pulse-Glasses with a laboratory ECG system to cross-validate HR data collected during various activities-sitting, talking, and walking-performed by a participant. We used Pulse-Glasses to record HR data of a driver to test IoT functionalities of location services and BLE and cloud connectivity. The first set of results is promising and demonstrates the prospect of Pulse-Glasses in the field of cloud-connected BSN.

When using the available m-health systems, ECG data for a small duration is recorded and sent to a server for processing and arrhythmia detection. Since arrhythmia occurrence is not so frequent in early stages, a need is felt to develop a real time and continuous arrhythmia monitoring system on the phone itself. This paper provides a novel approach to detect QRS complexes from a high fidelity ECG data obtained from B.E.A.T. ® hardware for arrhythmia monitoring in real time. Our approach referred to as m-QRS uses continuous wavelet transform at its kernel and its efficiency is compared to that of Pan-Tompkins's which is a standard QRS detection algorithm widely used for arrhythmia detection. It was found that our algorithm uses lesser computation time when compared to Pan-Tompkins and was found to be mobile friendly. This provides an opportunity to develop further algorithms to perform continuous and real-time arrhythmia monitoring on affordable smartphones without internet dependability.

Speech-language pathologists (SLPs) frequently use vocal exercises in the treatment of patients with speech disorders. Patients receive treatment in a clinical setting and need to practice outside of the clinical setting to generalize speech goals to functional communication. In this paper, we describe the development of technology that captures mixed speech signals in a group setting and allows the SLP to analyze the speech signals relative to treatment goals. The mixed speech signals are blindly separated into individual signals that are preprocessed before computation of loudness, pitch, shimmer, jitter, semitone standard deviation and sharpness. The proposed method has been previously validated on data obtained from clinical trials of people with Parkinson disease and healthy controls.