Lower-limb exoskeletons (LLE) have proven to be effective tools in rehabilitation for diminishing gait impairments. However, modern LLE control systems based on the assistance-as needed concept face challenges in providing appropriate interaction torques to the user. This is especially true when it is necessary to make the exoskeleton transparent, i.e. with zero user robot interaction torques (τUR), even during walking speed variations, when no assistance is required. To address these limitations, in this work we designed a novel transparent control system that utilizes a baseline torque controller, which generates a torque profile based on τUR from previous strides, and drives the actuators, leading to a lower τUR, along with a synchronization layer comprising a pool of adaptive oscillators (AOs) to estimate the user's gait phase and adapt the controller to different walking speeds. Additionally, a zero-torque controller was used in parallel to the main torque controller to enhance users' volitional control and balance. The proposed controller was experimentally tested on a six-DoF LLE with eight healthy participants who walked for 200 gait strides at three walking speeds. The baseline torque controller was shown to adapt to different walking speeds and successfully improve the exoskeleton transparency in terms of user-robot interaction, reducing the average τUR by 40% of the entire exoskeleton and 70% of the hip joint at 0.8 m/s, and gait kinematics compared to our prior controller based on a zero impedance model.

A Spring Loaded Double Pantograph (SLDP) mechanism is presented for safe balance training in elderly individuals practicing Tai Chi exercises. As people age, maintaining balance becomes increasingly critical, yet fear of falling often prevents effective exercise, creating a counterproductive cycle that increases fall risk. This natural hesitation to push physical limits during solo practice highlights the need for reliable safety systems. This paper presents a mechanism that provides variable assistance through spring-loaded actuation, so that support and freedom of movement can be balanced in a way that is both effective and unobtrusive. Here, it will be shown that, although support and unrestricted movement are traditionally considered contradictory goals, the two can be achieved simultaneously through mechanical design and the level of assistance can be automatically regulated. In this system, the support mechanism can a) detect falls rapidly, b) provide up to 98.0% body weight support when needed, and c) remain imperceptible during normal exercise. First, the mechanical design principles and kinematic analysis of the double-pantograph structure are presented. Methods for experimental validation with 13 human subjects will be addressed, demonstrating the system’s effectiveness through quantitative metrics of support forces, workspace utilization, and energy efficiency during simulated falls in Tai Chi movements.

Robot-assisted rehabilitation is expected to reduce locomotor limitations of children and young adults with Cerebral Palsy (CP). However, to achieve this result, it is essential that the robot is transparent, allowing the user to move freely, and generate joint torques only when the exoskeleton joints significantly deviate from physiological gait patterns. Nevertheless, the development of transparent operation in lower-limb exoskeletons is still an open problem with several implementation challenges. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground exoskeleton designed for children and young adults with CP. The approach employs a feedback zero-torque controller with feedforward compensations for the exoskeleton's dynamics and actuators' impedance. We experimented the proposed system in five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff). The proposed transparent controller reduced the user-robot interaction torque and improved user gait kinematics relative to ExoOff. This is a significant step toward an overground gait training exoskeleton for CP population.

Age-related loss of mobility and an increased risk of falling remain major obstacles for older adults to live independently. Many elderly people lack the coordination and strength necessary to perform activities of daily living, such as getting out of bed or stepping into a bathtub. A traditional solution is to install grab bars around the home. For assisting in bathtub transitions, grab bars are fixed to a bathroom wall. However, they are often too far to reach and stably support the user; the installation locations of grab bars are constrained by the room layout and are often suboptimal. In this paper, we present a mobile robot that provides an older adult with a handlebar located anywhere in space - “Handle Anywhere”. The robot consists of an omnidirectional mobile base attached to a reposition able handlebar. We further develop a methodology to optimally place the handle to provide the maximum support for the elderly user while performing common postural changes. A cost function with a trade-off between mechanical advantage and manipulability of the user's arm was optimized in terms of the location of the handlebar relative to the user. The methodology requires only a sagittal plane video of the elderly user performing the postural change, and thus is rapid, scalable, and uniquely customizable to each user. A proof-of-concept prototype was built, and the optimization algorithm for handle location was validated experimentally.

Although telepresence assistive robots have made significant progress, they still lack the sense of realism and physical presence of the remote operator. This results in a lack of trust and adoption of such robots. In this paper, we introduce an Avatar Robot System which is a mixed real/virtual robotic system that physically interacts with a person in proximity of the robot. The robot structure is overlaid with the 3D model of the remote caregiver and visualized through Augmented Reality (AR). In this way, the person receives haptic feedback as the robot touches him/her. We further present an Optimal Non-Iterative Alignment solver that solves for the optimally aligned pose of 3D Human model to the robot (shoulder to the wrist non-iteratively). The proposed alignment solver is stateless, achieves optimal alignment and faster than the baseline solvers (demonstrated in our evaluations). We also propose an evaluation framework that quantifies the alignment quality of the solvers through multifaceted metrics. We show that our solver can consistently produce poses with similar or superior alignments as IK-based baselines without their potential drawbacks.

We have been faced with an unprecedented challenge in combating the COVID-19/SARS-CoV2 outbreak that is threatening the fabric of our civilization, causing catastrophic human losses and a tremendous economic burden globally. During this difficult time, there has been an urgent need for biomedical engineers, clinicians, and healthcare industry leaders to work together to develop novel diagnostics and treatments to fight the pandemic including the development of portable, rapidly deployable, and affordable diagnostic testing kits, personal protective equipment, mechanical ventilators, vaccines, and data analysis and modeling tools. In this position paper, we address the urgent need to bring these inventions into clinical practices. This paper highlights and summarizes the discussions and new technologies in COVID-19 healthcare, screening, tracing, and treatment-related presentations made at the IEEE EMBS Public Forum on COVID-19. The paper also provides recent studies, statistics and data and new perspectives on ongoing and future challenges pertaining to the COVID-19 pandemic.

Voice analysis is an emerging technology which has the potential to provide low-cost, at-home monitoring of symptoms associated with a variety of health conditions. While voice has received significant attention for monitoring neurological disease, few studies have focused on voice changes related to flu-like symptoms. Herein, we investigate the relationship between changes in acoustic features of voice and self-reported symptoms during recovery from a flu-like illness in a cohort of 29 subjects. Acoustic features were automatically extracted from "sick" and "well" visit data collected in the laboratory setting, and feature down-selection was used to identify those that change significantly between visits. The selected acoustic features were extracted from at-home data and used to construct a combined distance metric that correlated with self-reported symptoms (0.63 rank correlation). Changes in self-reported symptoms corresponding to 10% of the ordinal scale used in the study were detected with an area under the curve of 0.72. The results show that acoustic features derived from voice recordings may provide an objective measure for diagnosing and monitoring symptoms of respiratory illnesses.

Because of the rapid and serious nature of acute cardiovascular disease (CVD) especially ST segment elevation myocardial infarction (STEMI), a leading cause of death worldwide, prompt diagnosis and treatment is of crucial importance to reduce both mortality and morbidity. During a pandemic such as coronavirus disease-2019 (COVID-19), it is critical to balance cardiovascular emergencies with infectious risk. In this work, we recommend using wearable device based mobile health (mHealth) as an early screening and real-time monitoring tool to address this balance and facilitate remote monitoring to tackle this unprecedented challenge. This recommendation may help to improve the efficiency and effectiveness of acute CVD patient management while reducing infection risk.

The articles in this special section focus on flexible sensing and medical imaging for cerebro-cardiovascular health care services. Healthcare and disease management are receiving increasing attention. Cerebro-cardiovascular diseases (CCVDs) are the leading cause of death globally. Cerebrocardiovascular diseases include a variety of medical conditions that affect the blood vessels of the brain, the cerebral circulation, and the heart. The common presentations of CCVDs include an ischemic stroke or mini-stroke and sometimes a hemorrhagic stroke, heart failure, hypertensive heart disease, etc. The important contributing risk factors include high blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, and excessive alcohol consumption, among others. A rapidly growing field, biomedical and health engineering research for CCVDs is unique in that it involves a variety of specialties such as neurology, surgery, cardiology, psychology and rehabilitation, and must meet the growing need for sophisticated, up-to-date biomedical and health informatics on clinical data, diagnostic testing, and therapeutic issues.

Objective assessment of stroke survivors' upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold. First, this paper introduces and validates a new benchmark measurement of the amount of hand use based on data recorded by a motion capture system, the gold standard for human movement analysis. Second, this paper introduces a machine learning-based analytic pipeline that estimates the amount of hand use using data obtained from the wearable sensors and validates its estimation performance against the aforementioned benchmark measurement. Based on data collected from 18 neurologically intact individuals performing 11 motor tasks resembling various activities of daily living, the analytic results presented herein show that our new benchmark measure is reliable and responsive, and that the proposed wearable system can yield an accurate estimation of the amount of hand use (normalized root mean square error of 0.11 and average Pearson correlation of 0.78). This study has the potential to open up new research and clinical opportunities for monitoring hand function in ambulatory settings, ultimately enabling evidence-based, patient-centered rehabilitation and healthcare.

This study presents preliminary results of a broader research on Home Robot monitoring for elder people. The final goal of the project is the development of a robot that works in synergy with an automatic fall detection device, reaching the patient and checking his condition in case of triggered alarm. This paper covers the initial steps necessary for the design of the tracking network which provides the machine with the subject's position, in particular the single node performance. The network is based on Ultra-Wide Band (UWB) wireless transceivers that in this study are the Decawave EVB1000 evaluation boards. Two types of analysis have been performed on the anchor: a Line of Sight (LOS) baseline accuracy and interference robustness. The results demonstrate that, for LOS distance estimation, to achieve a margin of error below 15 cm, the node has to be closer than 12 m to the target. If we remove the line of sight condition, introducing a subject walking straight between the two anchors, the error is spread in the order of 10 cm from the original baseline for a 10 m nodes distance recording. If the path is obstructed instead by a subject walking perpendicularly to the nodes instead leads to a different types of perturbations, with an absolute error below 13 cm.

Motivated by a need for accurate assessment and monitoring of patients with knee osteoarthritis in an ambulatory setting, a wearable electrogoniometer composed of a knee angular sensor and a three-axis accelerometer placed on the thigh is developed. Accurate assessment of knee kinematics requires accurate detection of walking amongst dynamic, heterogeneous, and individualized activities of daily living. This paper investigates four different machine learning techniques for detecting occurrences of walking in uncontrolled environments based on a dataset collected from a total of 4 healthy subjects. Multi-class classifier (random forest) based detection method showed the best performance, which supports 90% precision and 75% recall. The in-depth analysis and interpretation of the results show that accurate decision boundaries are necessary between 1) fast walking and descending stairs, 2) slow walking and ascending stairs, as well as 3) slow walking and transitional activities. This work provides a systematic approach to detect occurrences of walking in uncontrolled living conditions, which can also be extended to other activities.

The articles in this special section are focused on two major aspects of Internet of things (IoT) technologies for smart and connected health services (SCH): 1) monitoring and assisting individuals by means of smart systems including sensors, devices, and robotics; and 2) creating interoperable digital health information infrastructures to increase medical/health information availability and use. The papers published in this SI provide evidence that SCH tools that rely upon IoT technologiescould significantly improve clinical outcomes and thequality of life of individuals undergoing monitoring.

Periodic assessments of motor function in children with Cerebral Palsy can enable clinicians to make more informed decisions about the type and timing of treatment interventions. Current clinical practice is limited to sporadic assessments performed in a clinical environment and hence, not suitable for capturing small changes that occur longitudinally. We have developed a shoe-based wearable sensor system that allows unobtrusive long-term collection of center of pressure data in the home setting. So far the shoe-based system has been used to collect data from 15 subjects under supervised and semi-supervised settings. In this paper, we present a novel methodology, based on the analysis of center of pressure trajectories using Active Shape Models, for automated clinical assessment of gait deviations in children with Cerebral Palsy. We show that Active Shape Models can be used to effectively model characteristics of the center of pressure trajectories that are associated with specific aspects of gait deviations. A support vector machine classifier, trained on features derived from the Active Shape Models, is able to achieve an accuracy of greater than 90% at classifying clinical scores of gait deviation severity.

Design and control of an active knee rehabilitation orthotic system called ANdROS that was designed as a wearable and portable gait rehabilitation tool is presented. A corrective force field that reinforces a desired gait pattern is applied to the patient's impaired leg around the knee joint via an impedance controlled exoskeleton. The impedance controller is synchronized with the patient's walking phase which is estimated from the kinematic measurements of the healthy leg. The performance of the controller is evaluated through bench-testing.

The Robotic Gait Rehabilitation (RGR) Trainer, was designed and built to target secondary gait deviations in patients post - stroke. Using an impedance control strategy and a linear electromagnetic actuator, the device applies a force field to control pelvic obliquity through an orthopedic brace while the patient ambulates on treadmill. Healthy human subject testing confirmed efficacy of the method to impart significant gait restoration forces as a response to abnormal pelvic obliquity (hip hiking). This novel approach to application of force fields using endpoint impedance controlled linear actuators takes into account soft tissue compliance.

A newly developed hand rehabilitation system is presented that combines robotics and interactive gaming to facilitate repetitive performance of task specific exercises for patients recovering from neurological motor deficits. A two degree of freedom robotic interface allows coordinated motions of the forearm and the hand (pronation/supination and grasp/release, respectively). It is driven by two novel Electro-Rheological Fluid based hydraulic actuators. Tests were conducted to characterize these actuators, and feed-forward controllers were developed for their force/torque control. A virtual reality environment (maze game) was developed in which the robot applies force fields to the user as the user navigates the environment, forming a haptic interface between the patient and the game.

We present MercuryLive, a web-enhanced extension to a body sensor network platform for continuous home-based body motion sensing, interactive supervised data collection sessions, and long-term activity data analysis. The major goal of MercuryLive is to enable practical long-term health monitoring in a home setting and henceforth reduce the effort and cost for collecting clinically relevant quantitative measures on patients' health conditions during daily activities. MercuryLive contains three tiers: a central web server for streaming and storage of sensor data, a sensor data collection engine, and a user-friendly web-based GUI client. The platform is currently used in clinical studies on Parkinson's disease.

Quantitative assessment of motor abilities in stroke survivors can provide valuable feedback to guide clinical interventions. Numerous clinical scales were developed in the past to assess levels of impairment and functional limitation in individuals after stroke. The Functional Ability Scale is one of these clinical scales. It is a 75-point scale used to evaluate the functional ability of subjects by grading movement quality during performance of 15 motor tasks. Performance of these motor tasks requires subjects to reach for objects (e.g., a pencil on a table) and manipulate them (e.g., lift the pencil). In this paper, we show that accelerometer data recorded during performance of a subset of the motor tasks pertaining to the Functional Ability Scale can be relied upon to derive accurate estimates of the scores provided by a clinician using this scale. Accelerometer-based estimates of clinical scores were obtained by segmenting the recordings into movement components (reaching, manipulation, release/return), extracting data features, selecting features that maximized the separation among classes associated with different clinical scores, feeding these features to Random Forests to estimate scores for individual motor tasks, and using a linear equation to estimate the total Functional Ability Scale score based on the sum of the clinical scores for individual motor tasks derived from the accelerometer data. Results showed that it is possible to achieve estimates of the total Functional Ability Scale score marked by a bias of only 0.04 points of the scale and a standard deviation of only 2.43 points when using as few as three sensors to collect data during performance of only six motor tasks.

The 12 papers in this special section are original and relevant contributions in the area of smart wearable devices (SWDs) applied to the health and civil protection domain.

This special section on personal health systems (PHSs) features 13 papers in three main areas: new-micro-nano instrumentation, sensors, and sensor-based systems; new information processing technology via embedding intelligence in PHS; and PHS platforms to address specific clinical applications.

This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson's Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies.

This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

This paper presents a novel, smart and portable active knee rehabilitation orthotic device (AKROD) designed to train stroke patients to correct knee hyperextension during stance and stiff-legged gait (defined as reduced knee flexion during swing). The knee brace provides variable damping controlled in ways that foster motor recovery in stroke patients. A resistive, variable damper, electro-rheological fluid (ERF) based component is used to facilitate knee flexion during stance by providing resistance to knee buckling. Furthermore, the knee brace is used to assist in knee control during swing, i.e. to allow patients to achieve adequate knee flexion for toe clearance and adequate knee extension in preparation to heel strike. The detailed design of AKROD, the first prototype built, closed loop control results and initial human testing are presented here

The goal of this project is to develop wireless sensors and analysis methods to monitor patients with various motor dysfunctions. We are currently targeting two specific applications: facilitating medication titration in patients with Parkinson's disease and assessing motor recovery in stroke survivors undergoing rehabilitation. In our vision, the treatment and rehabilitation hospital of the future will allow clinicians to continuously monitor motor activity in patients via miniature sensor technology in order to better design interventions on an individual basis. Two key points toward developing the tools necessary to achieve continuous monitoring of motor function are (1) development of a robust and deployable wearable wireless network of sensors and (2) the development of analysis techniques to derive clinically relevant information from miniature sensor data.

The identification of the timing of the discharges of groups of muscle fibers (motor units) is of utmost importance in research into the strategies employed by the central nervous system in producing muscle force as well as in the clinical diagnosis of neuromuscular diseases. The process involves the recognition of unique shapes (action potentials) contributed by different motor units at random times throughout a muscle contraction. This paper addresses a specific aspect, of the identification process: the decomposition of the compound signal when the action potentials of two or more motor units are superimposed. We propose a cross-time-frequency-based procedure to identify which two (out of a previously identified collection of waveforms) are included in a superposition. The procedure also determines the relative delay of the two waveforms.