This study aimed to investigate the efficacy of manual therapy and exercise versus synchronized telerehabilitation with self-manual therapy and exercise in treating Subacromial Pain Syndrome (SPS). Sixty individuals diagnosed with SPS, aged 18-50 years, were randomly assigned to home exercise (HE), manual therapy (MT), and telerehabilitation (TR) groups. Treatment protocols were administered over 8 weeks and included specific exercises and therapy interventions. Out-come measures included Visual Pain Scale (VAS), shoulder range of motion (ROM) via gonio-metric measurements, Quick Disability Arm-Shoulder-Hand Problems Survey (Q-DASH), and patient satisfaction. Results revealed that both MT and TR groups exhibited reduced pain, in-creased ROM, lower Q-DASH scores, and higher patient satisfaction compared to the HE group. However, no significant differences were found between MT and TR groups in pain levels, ROM, Q-DASH scores, or patient satisfaction. The study concludes that both telerehabilitation and manual therapy effectively alleviate pain and are well-received by patients with SPS. Addition-ally, manual therapy demonstrates superiority in enhancing functional levels compared to exer-cise-based interventions. (Registration: NCT05200130)

Background: Tele-rehabilitation has grown significantly in the past years, especially in 2020 when it has been a crucial tool for supporting patients during the COVID-19 pandemic. Within the context of tele-rehabilitation, serious games have a significant role. However, realizing software for serious games capable of responding to the variety of user needs is resource demanding. Methods: we present Proteo, a modular framework for developing serious games from scratch, but with the ability of providing a high-level interface for game customization by therapists and researchers. We also present two serious game implementation examples with analysis of end user’s and therapists/researchers’ satisfaction. Results: by involving a group of 11 specialized therapists and 9 end users we analyzed the Proteo user’s satisfaction. We found that therapists and end users scored high level of involvement, and the therapists scored also high level of suitability. More in depth, both groups showed significant differences between positive and negative feeling, with positive feeling scoring higher than negative ones. Finally, concerning Users’ level of suitability the condition of successfulness of the system, ability to control, clarity and helpfulness were reported as high while the difficulty of the system and the difficulty of the task were reported as low. Conclusions: the proposed framework is a step forward in providing a comprehensive open-source, modular framework, to develop serious games for tele-rehabilitation. Proteo is distributed with a MIT license and available to researchers on GitHub and has been well accepted by the users we involved in the evaluation tests.

This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. On the server side, the therapist’s rehabilitation exercises are quantitatively assessed using a monocular passive vision system. The resultant analytical metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. On the client side, the exoskeleton utilizes these analytical metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilizing EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. This calibration mechanism is integrated into simulations of both upper and lower limb exoskeletons. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements for various exercises, including shoulder flexion, elbow flexion, and rowing-like movements. This work also includes computer simulations for tracking the control of rehabilitation exercises, demonstrating the adaptability of the system in reliably, robustly, and effectively following desired trajectories.

In recent years, the need to contain healthcare costs due to the growing public debt of many countries, combined with the need to reduce costly travel by patients unable to move autonomously, have captured the attention of public administrators towards tele-rehabilitation. This trend has been consolidated overwhelmingly following the Covid-19 pandemic, which has made it precarious, difficult and even dangerous for patients to access hospital facilities. We present a platform devoted to the rapid prototyping of Virtual Reality based, cognitive tele-rehabilitation exercises. Patients who experienced injury or pathology need to practice a continuous training in order to recover functional abilities, and the therapist need to monitor the outcomes of such practices. The Virtual Reality exercises are designed on Unity 3D to empower the therapist to set up personalised exercises in a easy way, enabling the patient to receive personalized stimuli, which are crucial for a positive outcome of the practice. Furthermore, the reaction speed of the system is of fundamental importance, as the temporal evolution of the scene must proceed parallel to the patient’s movements, to ensure an effective and efficient therapeutic response. So, we optimized the Virtual Reality application in order to make the loading phase and the startup phase as fast as possible and we have tested the results obtained with many devices: in particular computers and smartphones with different operating systems and hardware. The implemented platform integrates in Nu!Reha system®, a tele-rehabilitation set of services that helps patients to recover cognitive and functional capabilities.

Respiratory allergic diseases, affect over 500 million people globally and pose a substantial burden in terms of morbidity, mortality, and healthcare costs. Restrictive factors such as geographical disparities, infectious pandemics, limitations in resource, and shortage of allergy specialists in underserved areas, impede effective management. Moreover, the well-established suboptimal adherence in patients with respiratory allergies, contribute further to the accelerated loss of disease control. Telemedicine encompasses real-time visits, store-and-forward options triage, and computer-based technologies for establishing and efficient doctor-patient communication. Recent advances in digital technology, including designated applications, informative materials, digital examination devices, wearables, digital inhalers, and integrated platforms, facilitate personalized and evidence-based care delivery. The integration of telemonitoring in respiratory allergy care has shown beneficial effects in disease control, adherence, and quality of life. While the COVID-19 pandemic accelerated the adoption of telemedicine, certain concerns regarding technical requirements, platform quality, safety, reimbursement, and regulatory considerations remain unresolved. Challenges in telemonitoring implementation further include ensuring effective doctor-patient collaboration, addressing privacy concerns, and establishing cost-effective models. The integration of artificial intelligence (AI) in telemonitoring applications holds promise for data analysis, pattern recognition, and personalized treatment plans. Striking the balance between AI-enabled insights and human expertise is crucial for optimizing the benefits of telemonitoring. This overview reports the up- to- date telemedicine approaches in managing patients with respiratory allergies. While telemonitoring exhibits potential in enhancing patient care and healthcare delivery, critical considerations have to be addressed in order to ensure the successful integration of telemonitoring into the healthcare landscape.

Background: Health management is gaining importance as a business resource to enhance employee well-being. The rise of remote work during COVID-19 impacted lifestyle habits. This study explores the impact of tele-exercise intervention on remote workers' lifestyle habits, aiming to improve health and productivity. Methods: This study divided into remote (G1) and face-to-face (G2) groups. Over three months, G1 followed a 30-minute weekly exercise program. Measurements and questionnaires were collected at three time points covering areas like presenteeism, weight, sleep, stress, job satisfaction, and happiness. Data underwent Two-way repeated measures ANOVA, Autocorrelation, Correlation Analysis, and Correspondence Analysis to assess differences and relationships between G1 and G2. Results: ANOVA showed that a significant difference was observed in Daily Stress (DS) between G1 and G2, with G1 exhibiting higher DS. Autocorrelation Analyses showed for G1 that strong relationships each of BMI, Presenteeism (PST), Daily Stress (DS), Stress Relief (SR), Sleep Quality (SQ), Purpose of Work (PW), and Satisfaction of Life (SL) in the time frame. Correlation Analysis showed for G1 that the pairs of PRS and SQ, and DS and SL had strong positive relationships, and the pairs of BMI and PRS, and BMI and SQ had strong negative relationships. Autocorrelation Analyses showed for G2 that strong relationships each of SR, Stress due to Remote Work (SRW), ST, SQ, and PW in the time frame. Correspondence Analysis showed that that G1 was encompassed in a job and life satisfaction area, and G2 was encompassed in a sleep and stress for weight control area.

This paper address the development of a new technic in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90-minutes each. Each of these cycles is composed of a succession of several stages of sleep, more or less deep. The analysis of sleep is usually done using a polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG) and chin muscle tone by electromyography (EMG). The recording is done mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30-second epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: "awakening", "calm sleep" and "agitated sleep". The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYEL - SYstèmes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals.

Enhance®Fitness (EF) is an evidence-based exercise program recommended for management of osteoarthritis (OA). However, access to EF is limited in rural areas. Accordingly, we evaluated the feasibility and acceptability of remotely delivered EF in rural, community-dwelling older adults with symptomatic knee OA. A single arm pilot trial of remotely delivered EF classes was conducted. Videoconferencing was used to livestream the instructor-led, 1-hour EF classes 3 days/week for 12 weeks. Outcomes were assessed at baseline and immediately post-intervention. A total of 15 participants were enrolled with a median age of 70 years (interquartile range [IQR]: 67-75) and 14 (93%) were women. Median EF class attendance rate was 91% (IQR: 85-94%). Knee pain, as measured by the Knee injury and Osteoarthritis Outcome Score (KOOS), improved significantly from baseline to 12-week endpoint (mean difference=-11.4 [95% CI:-20.9, -2.0); P=0.02). In addition, participants’ self-reported knee function improved significantly (mean difference in KOOS Function score=-11.8 [95% CI:-18.4, -5.2]; P<0.01) as well as their physical capacity (mean difference in Timed Up and Go test time=1.8 seconds [95% CI: 0.2, 3.4]; P=0.03). All participants (100%) were very satisfied with remotely delivered EF classes and there were no serious adverse events. Findings from this pilot trial indicate that remotely delivered EF is feasible and acceptable in rural older adults with knee OA.

The use of haptic devices in rehabilitation of impaired limbs has become rather popular, given the proven effectiveness in promoting recovery. In a standard framework, such devices are used in rehabilitation centers, where patients interact with virtual tasks, presented on a screen. To track their sessions, kinematic/dynamic parameters or performance scores are recorded. However, as Internet access is now available at almost every home, and in order to reduce the hospitalization time of the patient, the idea of doing rehabilitation at home is gaining wide consent. Medical care programs can be synchronized with the home rehabilitation device; patient data can be sent to the central server that could redirect to the therapist laptop (tele-healthcare). The controversial issue is that the recorded data do not actually represent the clinical conditions of the patients according to the medical assessment scales, forcing them to frequently undergo clinical tests at the hospital. To respond to this demand, we propose the use of a bilateral master/slave haptic system that could allow the clinician, who interacts with the master, to assess remotely and in real time the clinical conditions of the patient that uses the home rehabilitation device as the slave. In this paper, we describe a proof of concept to highlight the main issues of such an application, limited to one degree of freedom, and to the measure of the stiffness and range of motion of the hand.

This investigation delves into the profound interplay between the e-commerce environment and the agricultural system, while scrutinizing the intricate human-land coupling dynamics engen-dered by transformations within the agricultural domain. Focusing on the expansion of orchards in Pinghe County, a pivotal epicenter for Chinese sweet pomelo production, this study eluci-dates the reverberations of Rural E-commerce Environment Development on Orchard Expansion through the lens of Tele-coupling. In doing so, it unveils the nuanced tapestry of the human-land coupling associations latent within the evolutionary trajectory of the agricultural system amidst the blossoming information society. The findings underscore a symbiotic relationship between the augmentation of orchard acreage and the burgeoning rural e-commerce landscape, effec-tively propelling the economic prosperity of the agricultural system. Moreover, a tele-coupling phenomenon has emerged, underscoring the intricate web of interconnections binding the mat-uration of the rural e-commerce ecosystem with the proliferation of orchards. The research illu-minates that the transformation of land utilization, imbricated within the agricultural system, manifests as a distinctive form of tele-coupling intricately woven into the fabric of urbanization and information technology advancements. Consequently, adopting a comprehensive theoreti-cal perspective, amalgamating disparate domains across distinct geographic realms, becomes imperative for discerning the intricate nuances of the human-land coupling nexus within these multifaceted, open systems.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

The advancements in the study of the human sense of touch are fueling the field of haptics. This is paving the way for augmenting the sensory perception during objects palpation in tele-surgery, and reproducing the information through tactile feedback. Here, we present a novel tele-palpation apparatus that enables the user to detect nodules with various distinct stiffness buried in an ad-hoc polymeric phantom. The contact force measured by the platform was encoded using a neuromorphic model and reproduced on the index fingertip of a remote user through a haptic glove embedding a piezoelectric disk. We assessed the effectiveness of this feedback in allowing nodule identification under two experimental conditions of real-time telepresence: In Line of Sight (ILS), where the platform was placed in the visible range of a user; and the more demanding Not In Line of Sight (NILS), with the platform being 50 km apart. We found that the entailed percentage of identification was higher for stiffer inclusions with respect to the softer ones (average of 74% within the duration of the task), in both telepresence conditions evaluated. These promising results call for further exploration of tactile augmentation technology for telepresence in medical interventions.

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; 2) The data, when communicated, are vulnerable to security and privacy issues; 3) The communication of the continuously collected data is not only costly but also energy hungry; 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks.This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a serviceoriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.