Estimation of human trunk movements by wearable strain sensors and improvement of sensor's placement on intelligent biomedical clothes

Sacroiliac Joint Dysfunction in Endurance Runners Using Wearable Technology as a Clinical Monitoring Tool: Systematic Review

BACKGROUND

In recent years, researchers have delved into the relationship between the anatomy and biomechanics of sacroiliac joint (SIJ) pain and dysfunction in endurance runners to elucidate the connection between lower back pain and the SIJ. However, the majority of SIJ pain and dysfunction cases are diagnosed and managed through a traditional athlete-clinician arrangement, where the athlete must attend regular in-person clinical appointments with various allied health professionals. Wearable sensors (wearables) are increasingly serving as a clinical diagnostic tool to monitor an athlete's day-to-day activities remotely, thus eliminating the necessity for in-person appointments. Nevertheless, the extent to which wearables are used in a remote setting to manage SIJ dysfunction in endurance runners remains uncertain.

OBJECTIVE

This study aims to conduct a systematic review of the literature to enhance our understanding regarding the use of wearables in both in-person and remote settings for biomechanical-based rehabilitation in SIJ dysfunction among endurance runners. In addressing this issue, the overarching goal was to explore how wearables can contribute to the clinical diagnosis (before, during, and after) of SIJ dysfunction.

METHODS

Three online databases, including PubMed, Scopus, and Google Scholar, were searched using various combinations of keywords. Initially, a total of 4097 articles were identified. After removing duplicates and screening articles based on inclusion and exclusion criteria, 45 articles were analyzed. Subsequently, 21 articles were included in this study. The quality of the investigation was assessed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) evidence-based minimum set of items for reporting in systematic reviews.

RESULTS

Among the 21 studies included in this review, more than half of the investigations were literature reviews focusing on wearable sensors in the diagnosis and treatment of SIJ pain, wearable movement sensors for rehabilitation, or a combination of both for SIJ gait analysis in an intelligent health care setting. As many as 4 (19%) studies were case reports, and only 1 study could be classified as fully experimental. One paper was classified as being at the "pre" stage of SIJ dysfunction, while 6 (29%) were identified as being at the "at" stage of classification. Significantly fewer studies attempted to capture or classify actual SIJ injuries, and no study directly addressed the injury recovery stage.

CONCLUSIONS

SIJ dysfunction remains underdiagnosed and undertreated in endurance runners. Moreover, there is a lack of clear diagnostic or treatment pathways using wearables remotely, despite the availability of validated technology. Further research of higher quality is recommended to investigate SIJ dysfunction in endurance runners and explore the use of wearables for rehabilitation in remote settings.

Musculoskeletal Rehabilitation: New Perspectives in Postoperative Care Following Total Knee Arthroplasty Using an External Motion Sensor and a Smartphone Application for Remote Monitoring

BACKGROUND

The number of total knee replacements performed annually is steadily increasing. Parallel options for postoperative care are decreasing, which reduces patient satisfaction. External devices to support physical rehabilitation and health monitoring will improve patient satisfaction and postoperative care.

METHODS

In a prospective, international multicenter study, patients were asked to use an external motion sensor and a smartphone application during the postoperative course of primary total knee arthroplasty. The collected data were transferred to a data platform, allowing for the real-time evaluation of patient data.

RESULTS

In three participating centers, 98 patients were included. The general acceptance of using the sensor and app was high, with an overall compliance in study participation rate of up to 76%. The early results showed a significant improvement in the overall quality of life (p < 0.001) and significant reductions in pain (p < 0.01) and depression (p < 0.001).

CONCLUSIONS

The early results of this clinical and multicenter study emphasize that there is a high interest in and acceptance of digital solutions in patients' treatment pathways. Motion sensor and smartphone applications support patients in early rehabilitation.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

Smart Shirt for Measuring Trunk Orientation

Improper cycling posture is linked to a variety of spinal musculoskeletal diseases, including structural malformation of the spine and back discomfort. This paper presents a novel smart shirt integrated tri-axial gyroscope and accelerometer that can detect postural variation in terms of spinal curvature changes. To provide accurate feedback to the wearer and improve the wearer's correct movement, the garment is able to recognize trunk body posture. The gyroscope/accelerometer was placed around the upper and mid trunk of the user to record tri-axial angular velocity data. The device can also be used to help determine the trunk bending angle and monitor body postures in order to improve optimal orientation and position. The garment enables continuous measurement in the field at high sample rates (50 Hz), and the sensor has a large measurement range (16 g, 2000°/s). As electronic components are non-washable, instead of encapsulating them, a detachable module was created. In this, magnets are embedded in the jersey, and allow the positioning and removal of the sensor. The test results show that the average trunk-bending angle was 21.5°, and 99 percent of the observed angle fell within the standard (ranging from 8° to 35°). The findings demonstrate the feasibility of employing the smart shirt sensor to estimate trunk motions in the field on a regular basis.

Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.

Recent Trends, Construction and Applications of Smart Textiles and Clothing for Monitoring of Health Activity: A Comprehensive Multidisciplinary Review

In the area of biomedical signal monitoring, wearable electronics represents a dynamically growing field with a significant impact on the market of commercial products of biomedical signal monitoring and acquisition, as well as consumer electronic for vital functions monitoring. Since the electrodes are perceived as one of the most important part of the biomedical signal monitoring, they have been one of the most frequent subjects in the research community. Electronic textile (e-textile), also called smart textile represents a modern trend in the wearable electronics, integrating of functional materials with common clothing with the goal to realize the devices, which include sensors, antennas, energy harvesters and advanced textiles for self-cooling and heating. The area of textile electrodes and e-textile is perceived as a multidisciplinary field, integrating material engineering, chemistry, and biomedical engineering. In this review, we provide a comprehensive view on this area. This multidisciplinary review integrates the e-textile characteristics, materials and manufacturing of the textile electrodes, noise influence on the e-textiles performance, and mainly applications of the textile electrodes for biomedical signal monitoring and acquisition, including pressure sensors, electrocardiography, electromyography, electroencephalography and electrooculography monitoring.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor

Low back pain (LBP) is the most common work-related musculoskeletal disorder among healthcare workers and is directly related to long hours of working in twisted/bent postures or with awkward trunk movements. It has already been established that providing relevant feedback helps individuals to maintain better body posture during the activities of daily living. With the goal of preventing LBP through objective monitoring of back posture, this paper proposes a wireless, comfortable, and compact textile-based wearable platform to track trunk movements when the user bends forward. The smart garment developed for this purpose was prototyped with an inductive sensor formed by sewing a copper wire into an elastic fabric in a zigzag pattern. The results of an extensive simulation study showed that this unique design increases the inductance value of the sensor, and, consequently, improves its resolution. Furthermore, experimental evaluation on a healthy participant confirmed that the proposed wearable system with the suggested sensor design can easily detect forward bending movements. The evaluation scenario was then extended to also include twisting and lateral bending of the trunk, and it was observed that the proposed design can successfully discriminate such movements from forward bending of the trunk. Results of the magnetic interference test showed that, most notably, moving a cellphone towards the unworn prototype affects sensor readings, however, manipulating a cellphone, when wearing the prototype, did not affect the capability of the sensor in detecting forward bends. The proposed platform is a promising step toward developing wearable systems to monitor back posture in order to prevent or treat LBP.

Application-Based Production and Testing of a Core-Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles

Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core–sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor’s piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities.

Automatic matching of surgeries to predict surgeons' next actions

OBJECTIVE

More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery.

MATERIAL AND METHOD

We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities. We propose an efficient algorithm to find the optimal partial alignment and a prediction system using maximum a posteriori probability estimation and filtering. We also introduce a weighting scheme allowing to improve the predictions by taking into account the relative similarity between the current surgery and a set of pre-recorded surgeries.

RESULTS

Our method is evaluated on two types of neurosurgical procedures: lumbar disc herniation removal and anterior cervical discectomy. Results show that our method outperformed the state of the art by predicting the next task that the surgeon will perform with 95% accuracy.

CONCLUSIONS

This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction.

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.

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.

Strain Sensors with Adjustable Sensitivity by Tailoring the Microstructure of Graphene Aerogel/PDMS Nanocomposites

Strain sensors with high elastic limit and high sensitivity are required to meet the rising demand for wearable electronics. Here, we present the fabrication of highly sensitive strain sensors based on nanocomposites consisting of graphene aerogel (GA) and polydimethylsiloxane (PDMS), with the primary focus being to tune the sensitivity of the sensors by tailoring the cellular microstructure through controlling the manufacturing processes. The resultant nanocomposite sensors exhibit a high sensitivity with a gauge factor of up to approximately 61.3. Of significant importance is that the sensitivity of the strain sensors can be readily altered by changing the concentration of the precursor (i.e., an aqueous dispersion of graphene oxide) and the freezing temperature used to process the GA. The results reveal that these two parameters control the cell size and cell-wall thickness of the resultant GA, which may be correlated to the observed variations in the sensitivities of the strain sensors. The higher is the concentration of graphene oxide, then the lower is the sensitivity of the resultant nanocomposite strain sensor. Upon increasing the freezing temperature from -196 to -20 °C, the sensitivity increases and reaches a maximum value of 61.3 at -50 °C and then decreases with a further increase in freezing temperature to -20 °C. Furthermore, the strain sensors offer excellent durability and stability, with their piezoresistivities remaining virtually unchanged even after 10 000 cycles of high-strain loading-unloading. These novel findings pave the way to custom design strain sensors with a desirable piezoresistive behavior.

A review of e-textiles in neurological rehabilitation: How close are we?

Textiles able to perform electronic functions are known as e-textiles, and are poised to revolutionise the manner in which rehabilitation and assistive technology is provided. With numerous reports in mainstream media of the possibilities and promise of e-textiles it is timely to review research work in this area related to neurological rehabilitation.This paper provides a review based on a systematic search conducted using EBSCO- Health, Scopus, AMED, PEDro and ProQuest databases, complemented by articles sourced from reference lists. Articles were included if the e-textile technology described had the potential for use in neurological rehabilitation and had been trialled on human participants. A total of 108 records were identified and screened, with 20 meeting the broad review inclusion criteria. Nineteen user trials of healthy people and one pilot study with stroke participants have been reported.The review identifies two areas of research focus; motion sensing, and the measurement of, or stimulation of, muscle activity. In terms of motion sensing, E-textiles appear able to reliably measure gross movement and whether an individual has achieved a predetermined movement pattern. However, the technology still remains somewhat cumbersome and lacking in resolution at present. The measurement of muscle activity and the provision of functional electrical stimulation via e-textiles is in the initial stages of development but shows potential for e-textile expansion into assistive technologies.The review identified a lack of high quality clinical evidence and, in some cases, a lack of practicality for clinical application. These issues may be overcome by engagement of clinicians in e-textile research and using their expertise to develop products that augment and enhance neurological rehabilitation practice.

Passive wireless sensor systems can recognize activites of daily living

The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.

A stretchable strain sensor based on a metal nanoparticle thin film for human motion detection

Wearable strain sensors for human motion detection are being highlighted in various fields such as medical, entertainment and sports industry. In this paper, we propose a new type of stretchable strain sensor that can detect both tensile and compressive strains and can be fabricated by a very simple process. A silver nanoparticle (Ag NP) thin film patterned on the polydimethylsiloxane (PDMS) stamp by a single-step direct transfer process is used as the strain sensing material. The working principle is the change in the electrical resistance caused by the opening/closure of micro-cracks under mechanical deformation. The fabricated stretchable strain sensor shows highly sensitive and durable sensing performances in various tensile/compressive strains, long-term cyclic loading and relaxation tests. We demonstrate the applications of our stretchable strain sensors such as flexible pressure sensors and wearable human motion detection devices with high sensitivity, response speed and mechanical robustness.

Sensitive, high-strain, high-rate bodily motion sensors based on graphene-rubber composites

Monitoring of human bodily motion requires wearable sensors that can detect position, velocity and acceleration. They should be cheap, lightweight, mechanically compliant and display reasonable sensitivity at high strains and strain rates. No reported material has simultaneously demonstrated all the above requirements. Here we describe a simple method to infuse liquid-exfoliated graphene into natural rubber to create conducting composites. These materials are excellent strain sensors displaying 10(4)-fold increases in resistance and working at strains exceeding 800%. The sensitivity is reasonably high, with gauge factors of up to 35 observed. More importantly, these sensors can effectively track dynamic strain, working well at vibration frequencies of at least 160 Hz. At 60 Hz, we could monitor strains of at least 6% at strain rates exceeding 6000%/s. We have used these composites as bodily motion sensors, effectively monitoring joint and muscle motion as well and breathing and pulse.