Inertia sensor-based guidance system for upperlimb posture correction

Functional movement screen dataset collected with two Azure Kinect depth sensors

This paper presents a dataset for vision-based autonomous Functional Movement Screen (FMS) collected from 45 human subjects of different ages (18–59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, quaternions, 3D human skeleton joints and 2D pixel trajectories of 32 joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 190 GB. This dataset provides the opportunity for automatic action quality evaluation of FMS.

Measurement(s)	3D joints coordinates • depth images
Technology Type(s)	depth sensors
Factor Type(s)	movement performance
Sample Characteristic - Organism	Homo sapiens

Sensors for Context-Aware Smart Healthcare: A Security Perspective

The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people's welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

An Improved Sensing Method of a Robotic Ultrasound System for Real-Time Force and Angle Calibration

An ultrasonic examination is a clinically universal and safe examination method, and with the development of telemedicine and precision medicine, the robotic ultrasound system (RUS) integrated with a robotic arm and ultrasound imaging system receives increasing attention. As the RUS requires precision and reproducibility, it is important to monitor the real-time calibration of the RUS during examination, especially the angle of the probe for image detection and its force on the surface. Additionally, to speed up the integration of the RUS and the current medical ultrasound system (US), the current RUSs mostly use a self-designed fixture to connect the probe to the arm. If the fixture has inconsistencies, it may cause an operating error. In order to improve its resilience, this study proposed an improved sensing method for real-time force and angle calibration. Based on multichannel pressure sensors, an inertial measurement unit (IMU), and a novel sensing structure, the ultrasonic probe and robotic arm could be simply and rapidly combined, which rendered real-time force and angle calibration at a low cost. The experimental results show that the average success rate of the downforce position identification achieved was 88.2%. The phantom experiment indicated that the method could assist the RUS in the real-time calibration of both force and angle during an examination.

Is Xbox 360 Kinect-based virtual reality training as effective as standard physiotherapy in patients undergoing breast cancer surgery?

PURPOSE

Breast cancer surgery may be associated with pain and physical symptoms in the upper limbs. Functional impairment and pain-related avoidance of movement may further increase disability level. This study aimed to investigate the potential effects of early postoperative virtual reality (VR) therapy on pain, range of motion (ROM), muscle strength, functionality, and fear of movement.

METHODS

Forty women with breast cancer who had undergone unilateral mastectomy with axillary lymph node dissection and who were receiving adjuvant therapy were included in the study and randomly assigned to two groups: the Kinect-based rehabilitation group (KBRG) and the standardized physical therapy group (SPTG). The KBRG (n = 20) received VR therapy using Xbox Kinect-based games and the SPTG (n = 20) received standard physiotherapy. Study subjects were assessed at baseline and after the 6-week treatment. Outcome measures were pain (visual analogue scale), grip strength (dynamometer), functionality (disabilities of the arm shoulder and hand questionnaire), muscle strength (handheld dynamometer), ROM (digital goniometer), and fear of movement (Tampa kinesiophobia scale (TKS)).

RESULTS

Both groups detected significant changes in pain, ROM, muscle strength, grip strength, functionality, and TKS scores after the treatment (p < 0.01). Fear of movement was significantly improved in the KBRG and the SPTG displayed more improvement in functionality (p < 0.05). There were no differences in ROM, muscle strength, grip strength, and pain between the groups after the treatment (p > 0.05).

CONCLUSION

Kinect-based VR therapy resulted in significant outcomes that were comparable to those obtained under standard physiotherapy in the early postoperative phase in patients who had undergone breast cancer surgery.

TRIAL REGISTRATION

This study is registered at ClinicalTrials.gov ( ClinicalTrials.gov identifier: NCT03618433).

Development and testing of a wearable wrist-to-forearm posture measurement system for hand-tool design evaluation

This study reports on the development and testing of a wearable wrist-to-forearm angle-measurement system for flexion/extension and radial/ulnar deviation of the wrist, and pronation/supination of the forearm. The system is based on inertial sensors and a microcontroller mounted on a glove and a forearm pad. The developed system was tested through the comparison of two off-the-shelf screwdrivers, one long and one short. Twelve male subjects participated in a within-subject experimental design test and performed a horizontal and a vertical screwing task for each screwdriver. Results indicated that the use of a long screwdriver causes significantly higher ulnar deviation of the wrist in both set-ups, while the short screwdriver promotes higher wrist extension in both set-ups. Clarity of the obtained results indicates that the proposed system is adequate for ergonomics studies on hand-tool design evaluation, while it addresses common pitfalls of other motion-capture methods.

Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.

Wearable systems for shoulder kinematics assessment: a systematic review

BACKGROUND

Wearable sensors are acquiring more and more influence in diagnostic and rehabilitation field to assess motor abilities of people with neurological or musculoskeletal impairments. The aim of this systematic literature review is to analyze the wearable systems for monitoring shoulder kinematics and their applicability in clinical settings and rehabilitation.

METHODS

A comprehensive search of PubMed, Medline, Google Scholar and IEEE Xplore was performed and results were included up to July 2019. All studies concerning wearable sensors to assess shoulder kinematics were retrieved.

RESULTS

Seventy-three studies were included because they have fulfilled the inclusion criteria. The results showed that magneto and/or inertial sensors are the most used. Wearable sensors measuring upper limb and/or shoulder kinematics have been proposed to be applied in patients with different pathological conditions such as stroke, multiple sclerosis, osteoarthritis, rotator cuff tear. Sensors placement and method of attachment were broadly heterogeneous among the examined studies.

CONCLUSIONS

Wearable systems are a promising solution to provide quantitative and meaningful clinical information about progress in a rehabilitation pathway and to extrapolate meaningful parameters in the diagnosis of shoulder pathologies. There is a strong need for development of this novel technologies which undeniably serves in shoulder evaluation and therapy.

Hand gesture monitoring using fiber-optic curvature sensors

A hand gesture monitoring system based on fiber-optic curvature sensors is developed. A glove is used as a carrier to support the monitoring system. According to the freedom of the hand joint, the layout of the sensors is established. An untreated fiber is introduced as a reference signal. The division operation between the sensing signal and reference signal is regarded as the monitoring result, which can reduce the monitoring error produced by external unstable factors. The experimental results show that the measurement errors are within the range of ±π/36 and ±π/75, respectively, when measuring the joint bending and abduction angle, which indicates the feasibility of the proposed monitoring system.

Interactive wearable systems for upper body rehabilitation: a systematic review

Background

The development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies.

Method

A systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010–April 2016).

Results

Forty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial.

Conclusions

This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

Usability Evaluation of a VibroTactile Feedback System in Stroke Subjects

Background

To increase the functional capabilities of stroke subjects during activities of daily living, patients receive rehabilitative training to recover adequate motor control. With the goal to motivate self-training by use of the arm in daily life tasks, a sensor system (Arm Usage Coach, AUC) was developed that provides VibroTactile (VT) feedback if the patient does not move the affected arm above a certain threshold level. The objective of this study is to investigate the usability of this system in stroke subjects.

Method

The study was designed as a usability and user acceptance study of feedback modalities. Stroke subjects with mild to moderate arm impairments were enrolled. The subjects wore two AUC devices one on each wrist. VT feedback was given by the device on the affected arm. A semi-structured interview was performed before and after a measurement session with the AUC. In addition, the System Usability Scale (SUS) questionnaire was given.

Results

Ten ischemic chronic stroke patients (39 ± 38 months after stroke) were recruited. Four out of 10 subjects have worn the VT feedback on their dominant, affected arm. In the pre-measurement interview, eight participants indicated a preference for acoustic or visual over VT feedback. In the post evaluation interview, nine of 10 participants preferred VT over visual and acoustic feedback. On average, the AUC gave VT feedback six times during the measurement session. All participants, with the exception of one, used their dominant arm more then the non-dominant. For the SUS, eight participants responded above 80%, one between 70 and 80%, and one participant responded below 50%.

Discussion

More patients accepted and valued VT feedback after the test period, hence VT is a feasible feedback modality. The AUC can be used as a telerehabilitation device to train and maintain upper extremity use in daily life tasks.

Haptic wearables as sensory replacement, sensory augmentation and trainer - a review

Sensory impairments decrease quality of life and can slow or hinder rehabilitation. Small, computationally powerful electronics have enabled the recent development of wearable systems aimed to improve function for individuals with sensory impairments. The purpose of this review is to synthesize current haptic wearable research for clinical applications involving sensory impairments. We define haptic wearables as untethered, ungrounded body worn devices that interact with skin directly or through clothing and can be used in natural environments outside a laboratory. Results of this review are categorized by degree of sensory impairment. Total impairment, such as in an amputee, blind, or deaf individual, involves haptics acting as sensory replacement; partial impairment, as is common in rehabilitation, involves haptics as sensory augmentation; and no impairment involves haptics as trainer. This review found that wearable haptic devices improved function for a variety of clinical applications including: rehabilitation, prosthetics, vestibular loss, osteoarthritis, vision loss and hearing loss. Future haptic wearables development should focus on clinical needs, intuitive and multimodal haptic displays, low energy demands, and biomechanical compliance for long-term usage.

Design and control of an active 1-DoF mechanism for knee rehabilitation

A 1-DoF robot is designed and fabricated to be used for knee rehabilitation training. The mechanism (robot) is designed to perform specific set of exercises while the patient is sitting on a chair. The therapy process for patients has different stages; each stage consists of specific exercises to recover the knee to its condition before injury. The maximum torque of healthy joint during the extension/flexion exercise is evaluated by simulation and suitable actuator is selected based on the results. A prototype is then fabricated as a platform to evaluate the design and control concepts. The experiment procedure consisting of three stages of therapy indicates good tracking performance and safe operation of the system. Implication for Rehabilitation A 1-DoF mechanism for knee rehabilitation has been designed to perform three stages of therapy: passive, active assist and active resist. The assistive and resistive torque, during active assist and active resist stages, can be set according to the progress in therapy. The results of this study suggest the system has the potential to result in various benefits including reduction of physical workload of physiotherapists and improved repeatability.