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Components of a Rehabilitation-Internet-of-Things: wireless chargers for sensors (1), ankle accelerometers with gyroscopes (2) and Android phone (3) to monitor walking and cycling, and a force sensor (4) in line with a stretch band (5) to monitor resistance exercises.
Source publication
Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the typ...
Contexts in source publication
Context 1
... this stage of consideration for a successful RIoT, low cost, user-friendliness, and reliability for monitoring basic motor learning, exercise and fitness strategies seems the most practical direction. We successfully tested a key component of an RIoT, a remote motion sensor system for walking and cycling that includes a triaxial accelerometer and gyroscope worn on each ankle (see Figure 1). 44 In this randomized clinical trial called SIRRACT, 140 inpatient stroke rehabilitation subjects from 16 sites on 4 continents were given feedback about 10-m walking speed twice a week or enhanced feedback about daily walking speeds, distance, and duration of bouts from sensor-derived data. ...
Context 2
... In this randomized clinical trial called SIRRACT, 140 inpatient stroke rehabilitation subjects from 16 sites on 4 continents were given feedback about 10-m walking speed twice a week or enhanced feedback about daily walking speeds, distance, and duration of bouts from sensor-derived data. 44 For our goal of increasing self-managed practice and fit- ness after stroke, we are also testing the user friendliness of a bundle of potential mHealth devices, including a heart rate monitor, an instrumented resistance exercise band (see Figure 1), a pedaling ergometer for bed and floor, and a small box with sensor that makes a virtual reality trans- formation of reach-to-pinch or grasp practice tasks. Our experience may help others develop their direction for motor training research. ...
Context 3
... raw data from the $100 ankle sensors is collected from the start of the day at home until bedtime, then trans- mitted overnight by Bluetooth radio to a smartphone that sits on a night table (Figure 1). Patients do not carry the phone during the day. ...
Context 4
... home-based strengthening exercise, we have con- nected a Theraband or other type of stretchable resistance cord in series with a force sensor that has Bluetooth output to the smartphone that also transmits ankle sensor data (Figure 1). During concentric and eccentric resistance exer- cises for one or both arms and legs, the duration and force is recorded. ...
Citations
... Similarly, the frequency band must also be limited for MI, in order to avoid mixing the estimated signal-level relationships of underlying neural processes reflected at different frequency bands, which would veil the actual signal interdependence. Consequently, we divided the EEG data into the following frequency bands: delta (0.5-4 Hz), theta (4-7 Hz), low gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), and high gamma (45-60 Hz). Then, we estimated the functional connectivity for each of the bands independently. ...
... EEG is, in conjunction with machine learning, transforming the field of rehabilitation. This potent combination facilitates a deeper understanding of brain activity, offers more precise predictions for recovery outcomes, and enables the personalization of treatment plans [37]. Brain connectivity analysis can, by itself, contribute to the understanding of neural processes. ...
The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca’s aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum). Across eight participants, employing leave-one-out validation for each, we evaluated the intersubject prediction accuracy across all connectivity methods and frequency bands. GC, MI theta, and PLV low-gamma emerged as the top performers, achieving 89.4%, 85.8%, and 82.7% accuracy in classifying verbal working memory task data. Intriguingly, measures designed to eliminate volume conduction exhibited the poorest performance in predicting rehabilitation-induced brain changes. This observation, coupled with variations in model performance across frequency bands, implies that different connectivity measures capture distinct brain processes involved in rehabilitation. The results of this paper contribute to current knowledge by presenting a clear strategy of utilizing limited data to achieve valid and meaningful results of machine learning on post-stroke rehabilitation EEG data, and they show that the differences in classification accuracy likely reflect distinct brain processes underlying rehabilitation after stroke.
... For delay-sensitive tasks, when the amount of data is huge or complex, the method of task offloading is of great significance, which can reduce delay and power consumption, and achieve load balancing (Dobkin 2017). Effective and rapid task offloading is the key to guaranteeing QoS. ...
In order to improve the effect of short video transmission, this paper constructs the overall performance model of the node based on DNC theory. DNC theory is based on the mathematical foundation of minimal plus algebra. Moreover, it analyzes network QoS through two basic tools: arrival curve and service curve, and finds the upper bound of network performance parameters based on network behavior. In addition, it implements the problem of task offloading in fog computing, and needs to calculate the definite boundary of the network delay, backlog and other parameters in the worst case, which is convenient for the realization of the MOTO and GOMOTO algorithms. Finally, this paper combines the Internet of Things technology and short video transmission methods to construct a short video transmission model based on the Internet of Things node technology, and designs experiments to verify the effects of the model proposed in this paper. It can be seen from the experimental research that the IoT node technology proposed in this paper can effectively improve the effect of short video transmission and effectively increase the speed of short video transmission.
... While there is evidence supporting the importance of frequent but shorter periods of exercise and motor skill training and their continuation [18,19], compliance (adherence) with in-home rehabilitation programs may decline in the absence of real-time therapeutic feedback [20][21][22]. The solution for many individuals is telerehabilitation consisting of virtual reality or video game-based systems equipped with motion sensors (e.g., inertial measurement unit (IMU)) and depth cameras (e.g., Kinect) or instrumented boards (e.g., Wii Balance Board) [23][24][25][26]. ...
Recent advances in wearable motion sensors, mobile devices, the Internet of Things, and telecommunications have created new potential for telerehabilitation. Recognizing that there is no systematic review of smartphone- or tablet-based balance and gait telerehabilitation technology for long-term use (i.e., four weeks or more), this systematic review summarizes the effects of smartphone- or tablet-based rehabilitation technology on balance and gait exercise and training in balance and gait disorders. The review examined studies written in English published from 2013 to 2023 in Web of Science, Pubmed, Scopus, and Google Scholar. Of the 806 studies identified, 14 were selected, and the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was applied to evaluate methodological quality. The systematic review concluded that all 14 studies found balance and gait performance improvement after four weeks or more of balance and gait telerehabilitation. Ten of the 14 studies found that carry-over effects (improved functional movements, muscle strength, motor capacity, cognition, and reduced fear of falling and anxiety levels) were maintained for weeks to months. The results of the systematic review have positive technical and clinical implications for the next-generation design of rehabilitation technology in balance and gait training and exercise programs.
... Several preliminary efforts have been reported in the literature relating to robotic upper limb home-based rehabilitation. A homebased approach based on Internet-of-Things (IoT) has been proposed [22] , but this IoT based approach involves manual physical therapy instead of a robotic rehabilitation system. Home-based robotic bilateral upper limb rehabilitation systems have been reported in the literature that can adjust the robot applied assistance based on the feedback on electromyography (EMG) signals collected from the arms of the patients [23][24][25] . ...
Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.89180,2.67530 and 8.02580, respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.
... The significant decrease in the number of IMT publications in 2021 compared with 2020 may be due to the impact of the COVID-19 epidemic, which prevented IMT-related clinical studies from being conducted as normal, affecting research progress [36]. IMT publications rebounded significantly in 2022, probably due to normalization of COVID-19 epidemic control and the relatively well-developed implementation of remote home rehabilitation [37][38][39]. The advantages of remote home rehabilitation, which include lack of geographical restrictions and savings in time and cost, may promote development of IMT-related research during the ongoing COVID-19 epidemic. ...
Background
Inspiratory muscle training (IMT) aims to train inspiratory muscles based mainly on the diaphragm by applying a load resistance during the inspiratory process. Many papers related to IMT have been published in various journals; however, no articles objectively and directly present the development trends and research hotspots of IMT. Therefore, this study used CiteSpace to visually analyze recent IMT-related publications to provide valuable information for future IMT-related studies.
Material/Methods
CiteSpace was applied to analyze the IMT-related publications by countries, institutions, journals, authors, references, and keywords.
Results
We included 504 papers. The number of IMT-related publications trended upward between 2009 and 2022. Leuven had the highest number of publications by an institution. The American Journal of Respiratory and Critical Care Medicine was the most frequently co-cited journal. Half of the top 10 references cited were from Journal Citation Reports (JCR) Q1 and half were about the application of IMT in chronic obstructive pulmonary disorder. Gosselink was the author with the highest number of publications and Aldrich was the author with the highest co-citation frequency. The preponderance of studies on the surgical population and postoperative pulmonary complications reflects potential application of IMT in enhanced recovery after surgery.
Conclusions
This study provides scholars with important information related to IMT research. It analyzes IMT research trends and status, which can help researchers identify primary topics in the field and find ways to explore new research directions to promote the application of IMT in clinical practice and the cooperation of IMT-related disciplines.
... Thus, the longer the rehabilitation time, the more the subjects feel self-confident, and consequently, their will to exercise at home increases. It is well accepted that repeated practice, such as during an exercise training period, increases the subject's confidence and motor learning [28][29][30]. In our case, the rehabilitation sessions allowed the subjects to become more familiar with the exercises over time, thus, improving the perception of their ability and performance. ...
Background:
Interactive videogames, virtual reality, and robotics represent a new opportunity for multimodal treatments in many rehabilitation contexts. However, several commercial videogames are designed for leisure and are not oriented toward definite rehabilitation goals. Among the many, Playball® (Playwork, Alon 10, Ness Ziona, Israel) is a therapeutic ball that measures both movement and pressure applied on it while performing rehabilitation games. This study aimed: (i) to evaluate whether the use of this novel digital therapy gaming system was clinically effective during shoulder rehabilitation; (ii) to understand whether this gaming rehabilitation program was effective in improving patients' engagement (perceived enjoyment and self-efficacy during therapy; attitude and intention to train at home) in comparison to a control non-gaming rehabilitation program.
Methods:
A randomized controlled experimental design was outlined. Twenty-two adults with shoulder pathologies were recruited for a rehabilitation program of ten consecutive sessions. A control (CTRL; N = 11; age: 62.0 ± 10.9 yrs) and an intervention group (PG; N = 11; age: 59.9 ± 10.2 yrs) followed a non-digital and a digital therapy, respectively. The day before (T0) and after (T1) the rehabilitation program, pain, strength, and mobility assessments were performed, together with six questionnaires: PENN shoulder Score, PACES-short, Self-efficacy, Attitudes to train at home, Intention to train at home, and System usability scale (SUS).
Results:
MANOVA analysis showed significant improvements in pain (p < 0.01), strength (p < 0.05), and PENN Shoulder Score (p < 0.001) in both groups. Similarly, patients' engagement improved, with significant increments in Self-efficacy (p < 0.05) and attitude (p < 0.05) scores in both groups after the rehabilitation. Pearson correlation showed significant correlations of the Δ scores (T1 - T0) between PACES and Self-efficacy (r = 0.623; p = 0.041) and between PACES and Intention to train at home (r = 0.674; p = 0.023) only in the PG. SUS score after the rehabilitation (74.54 ± 15.60) overcame the cut-off value of 68, representative of good usability of a device.
Conclusions:
The investigated digital therapy resulted as effective as an equivalent non-digital therapy in shoulder rehabilitation. The reported positive relationship between the subject's enjoyment during digital therapy and intention to train at home suggests promising results in possible patient's exercise engagement at home after the rehabilitation in the medical center.
Retrospectively registered:
NCT05230056.
... However, health care and rehabilitation services for wheelchair users often do not understand what happens in other contexts outside the care environment, as well as in patients' environmental factors, which makes monitoring and adequate therapeutic treatment difficult, generating delays and slowness in the rehabilitation process with decreased quality [7,8]. ...
Background:
Wheelchair positioning systems can prevent postural deficits and pressure injuries. Even so, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user.
Objective:
We present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things, for automated positions, as part of a study on wheelchairs and telemonitoring.
Methods:
A mixed method 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 five participants with descriptive analysis to design system concepts were used.
Results:
We presented a new wheelchair system with hardware and software components developed based on co-participation with singular components in an IoT architecture. The use of sensors from the Inertial Measurement Unit in an Internet of Things solution was essential to provide alternatives to carry out the feedback of the actual use of the tilt and recline functions in the wheelchair, autonomously and programmably via a smartphone application, as well as the need for 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.
... As in in-person post-stroke rehabilitation, a critical element of effective telerehabilitation is the ability to collect quantitative data about an individual's movement. 91 A number of recent technologies have aimed to meet this need. For instance, body sensors can identify the type and quantity of movement during practice or daily routines. ...
Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.
... When BMI > 24 kg/m 2 , the doctor will remind the participant to control their weight and send information about weight management and healthy eating. (b) Emotional management: patients with chronic pain and activity limitation are at a high risk of depression [37], and in this study, physical therapists push brochures to participants and communicate and guide them. Ensure adequate sleep (7-8 h a day), chat with friends every day, take a deep breath when troubled, listen to soothing light music when irritable, etc. ...
Background
Osteoarthritis (OA) is a common and highly disabling disease that imposes a heavy burden on individuals and society. Although physical therapy is recommended as an important method to relieve OA symptoms, patients cannot continue treatment after returning home. Research on Internet telerehabilitation for knee osteoarthritis (KOA) can reduce pain and improve patient quality of life, and Internet of Things (IoT)-based telerehabilitation is a new form of delivering rehabilitation. This study will evaluate the effect of telerehabilitation via IoT, as a medium to deliver exercises, on pain and walking in patients with KOA.
Methods
This study is a single-blind randomized controlled trial. We will recruit 42 middle-aged and elderly patients with KOA aged ≥ 50 years and randomly divided into power cycling group, neuromuscular exercise group, and control group, and intervention will last for 12 weeks. Outcome measures will be taken at baseline and 4 weeks, 8 weeks, and 12 weeks post-intervention. The pre- and posttreatment differences in knee pain and physical function between participants undergoing power cycling and neuromuscular training and those in the control group will be determined by each scale. The effectiveness will be assessed by the Western Ontario and McMaster Universities Osteoarthritis Index Score (WOMAC) and an 11-point numerical pain rating scale. Walking function and quality of life will be assessed by the timed up and go and walk test, 6-min walk test, and quality of life health status questionnaires.
Discussion
The findings from this trial will establish the feasibility and effectiveness of IoT-based power cycling and neuromuscular training on elderly patients with KOA in the community. As a result, this trial may help provide experimental evidence for finding a better exercise method suitable for elderly patients with KOA in the community.
Trail registration
Chinese Clinical Trials Registry ChiCTR2200058924. Prospectively registered on 6 May 2022.
... Using smartphones as the basis for a patient training regimen is beneficial because it facilitates ondemand exercise guidance and continuation of patient training after discharge [38,39]. A simple-to-use, programmable tool that offers feedback on the degree and direction of movement should be utilized in conjunction with equipment that can be configured for home use [40]. While certain commercial gaming systems, such as the Nintendo Wii or Kinect [10,41], have been harnessed to offer visual feedback to stroke patients, access to equipment and the use of video games to train patients may be limited due to cost [42]. ...
... In contrast, most participants already owned a smartphone upon entering the study. Thus, use of the smartphone as a feedback device in conjunction with a low-cost balance disc improves training accessibility [40]. Furthermore, the smartphone applications that were employed in the study were free to participants and easy to use [43]. ...
Background
Sitting ability is critical for daily activities in individuals who have experienced a stroke. A combination of seated balance training on an unstable surface and real-time visual feedback via a simple mobile inclinometer application may improve trunk control in stroke survivors.
Objective
This randomized controlled trial aimed to determine the effects of home-based exercise utilizing a balance disc with input from a smartphone inclinometer application on sitting balance and activities of daily living in stroke survivors.
Methods
This trial enrolled 32 stroke survivors aged 30 to 75 years. Participants were randomly assigned to one of two groups: intervention or control. Both groups underwent four weeks of traditional therapy. Additionally, the intervention group received four weeks of multidirectional lean training utilizing a balance disc and a smartphone application with an inclinometer. The Postural Assessment Scale for Stroke (PASS), the Function in Sitting Test (FIST), and the Barthel Index (BI) were used to assess the results. To compare between group effects, an ANCOVA analysis was performed using a baseline as a covariate.
Results
The PASS changing posture and BI were considerably greater in the intervention group compared to the control group. Other metrics revealed no statistically significant differences between the groups.
Conclusion
Home-based training with balance discs and input from a smartphone inclinometer application may improve postural control and daily activity in stroke patients.
Trial registration
Clinical trials registry number: TCTR20210617004 .
