IoT Services and Applications in Rehabilitation: An Interdisciplinary and Meta-Analysis Review

An Automated Electronic System in a Motorized Wheelchair for Telemonitoring: Mixed Methods Study Based on Internet of Things

BACKGROUND

Wheelchair positioning systems can prevent postural deficits and pressure injuries. However, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user.

OBJECTIVE

This study aims to present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things (IoT), for automated positioning as part of a study on wheelchairs and telemonitoring.

METHODS

We conducted a mixed methods study with a user-centered design approach, interviews with 16 wheelchair users and 66 professionals for the development of system functions, and a formative assessment of 5 participants with descriptive analysis to design system concepts.

RESULTS

We presented a new wheelchair system with hardware and software components developed based on coparticipation with singular components in an IoT architecture. In an IoT solution, the incorporation of sensors from the inertial measurement unit was crucial. These sensors were vital for offering alternative methods to monitor and control the tilt and recline functions of a wheelchair. This monitoring and control could be achieved autonomously through a smartphone app. In addition, this capability addressed the requirements of real users.

CONCLUSIONS

The technologies presented in this system can benefit telemonitoring and favor real feedback, allowing quality provision of health services to wheelchair users. User-centered development favored development with specific functions to meet the real demands of users. We emphasize the importance of future studies on the correlation between diagnoses and the use of the system in a real environment to help professionals in treatment.

Internet of Things (IoT) Enables Robot-Assisted Therapy as a Home Program for Training Upper Limb Functions in Chronic Stroke: A Randomized Control Crossover Study

OBJECTIVE

To compare the effects of using an Internet of things (IoT)-assisted tenodesis-induced-grip exoskeleton robot (TIGER) and task-specific motor training (TSMT) as home programs for the upper-limb (UL) functions of patients with chronic stroke to overturn conventional treatment modes for stroke rehabilitation.

DESIGN

A randomized 2-period crossover study.

SETTING

A university hospital.

PARTICIPANTS

Eighteen chronic stroke patients were recruited and randomized to receive either the IoT-assisted TIGER first or TSMT first at the beginning of the experiment (N=18).

INTERVENTION

In addition to the standard hospital-based therapy, participants were allocated to receive a 30-minute home-based, self-administered program of either IoT-assisted TIGER first or TSMT first twice daily for 4 weeks, with the order of both treatments reversed after a 12-week washout period. The exercise mode of the TIGER training included continuous passive motion and the functional mode of gripping pegs. The TSMT involved various movement components of the wrist and hand.

MAIN OUTCOME MEASURES

The outcome measures included the box and block test (BBT), the Fugl-Meyer assessment for upper extremity (FMA-UE), the motor activity log, the Semmes-Weinstein Monofilament test, the range of motion (ROM) of the wrist joint, and the modified Ashworth scale.

RESULTS

Significant treatment-by-time interaction effects emerged in the results for the BBT (F(1.31)=5.212 and P=.022), the FMA-UE (F(1.31)=6.807 and P=.042), and the ROM of the wrist extension (F(1.31)=8.618 and P=.009). The participants who trained at home with the IoT-assisted TIGER showed more improvement of their UL functions.

CONCLUSIONS

The IoT-assisted TIGER training has the potential for restoring the UL functions of stroke patients.

Effect of IoT-based power cycling and quadriceps training on pain and function in patients with knee osteoarthritis: A randomized controlled trial protocol

BACKGROUND

Knee osteoarthritis (KOA) is a chronic musculoskeletal disease affecting the entire joint. Exercise therapy is the core treatment plan for non-surgical treatment of KOA, and tele-rehabilitation is also applied to KOA, but there is a lack of research on the comparison of pain and function recovery between different exercise methods combined Internet respectively. The study aims to compare the effects of power cycling and quadriceps training combined with online guidance separately on KOA mitigation of pain, recovery of function, quality of life, and adherence of participants in the community, compared to the control group.

METHODS

This study is a single-blind, 12-week parallel randomized controlled trial. Seventy-two participants aged ≥ 50 years with KOA will be randomized into either the power cycling group, the quadriceps group or the control group. The intervention will be performed three times per week during 12 weeks. Outcome measures will be assessed at baseline, and at 4, 8, and 12 weeks after allocation. The primary outcome will be self-reported pain, assessed with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale. Secondary outcomes will include mitigation of knee pain, quality of life, improvement of functional physical performance, adherence of participants.

DISCUSSION

By summarizing the study's strengths and limitations, this trial results may guide tele-rehabilitation of KOA in the community.Trial registration: The study was registered in the clinical trial registry ChiCTR2200059255, 27/04/2022.

Development of an Intelligent System for the Monitoring and Diagnosis of the Well-Being

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their application in health is still limited to the following physical parameters to allow physicians treatment and diagnosis. This paper presents LM Research, a smart monitoring system mainly composed of a web page, REST APIs, machine learning algorithms, psychological questionnaire, and smartwatches. The system introduces the continuous monitoring of the users' physical and mental indicators to prevent a wellness crisis; the mental indicators and the system's continuous feedback to the user could be, in the future, a tool for medical specialists treating well-being. For this purpose, it collects psychological parameters on smartwatches and mental health data using a psychological questionnaire to develop a supervised machine learning wellness model that predicts the wellness of smartwatch users. The full construction of the database and the technology employed for its development is presented. Moreover, six machine learning algorithms (Decision Tree, Random Forest, Naive Bayes, Neural Networks, Support Vector Machine, and K-nearest neighbor) were applied to the database to test which classifies better the information obtained by the proposed system. In order to integrate this algorithm into LM Research, Random Forest being the one with the higher accuracy of 88%.

Internet of Things for beyond-the-laboratory prosthetics research

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

Deploying Serious Games for Cognitive Rehabilitation

The telerehabilitation of patients with neurological lesions has recently assumed significant importance due to the COVID-19 pandemic, which has reduced the possibility of access to healthcare facilities by patients. Therefore, the possibility of exercise for these patients safely in their own homes has emerged as an essential need. Our efforts aim to provide an easy-to-implement and open-source methodology that provides doctors with a set of simple, low-cost tools to create and manage patient-adapted virtual reality telerehabilitation batteries of exercises. This is particularly important because many studies show that immediate action and appropriate, specific rehabilitation can guarantee satisfactory results. Appropriate therapy is based on crucial factors, such as the frequency, intensity, and specificity of the exercises. Our work’s most evident result is the definition of a methodology that allows the development of rehabilitation exercises with a limited effect in both economic and implementation terms, using software tools accessible to all.

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing^® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms^-1, the device's power requirements (i.e., P¯=5.84 mW) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

Co-Creation Facilitates Translational Research on Upper Limb Prosthetics

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, hereafter called users, are yet to benefit from the fast-paced growth in academic knowledge within the field of upper limb prosthetics. Crucially over the past decade, research has acknowledged the limitations of conducting laboratory-based studies for clinical translation. This has led to an increase, albeit rather small, in trials that gather real-world user data. Multi-stakeholder collaboration is critical within such trials, especially between researchers, users, and clinicians, as well as policy makers, charity representatives, and industry specialists. This paper presents a co-creation model that enables researchers to collaborate with multiple stakeholders, including users, throughout the duration of a study. This approach can lead to a transition in defining the roles of stakeholders, such as users, from participants to co-researchers. This presents a scenario whereby the boundaries between research and participation become blurred and ethical considerations may become complex. However, the time and resources that are required to conduct co-creation within academia can lead to greater impact and benefit the people that the research aims to serve.