Review on design of real-time posture monitoring system for the cervical region.

In cervical health, the Posture Monitoring System (PMS) employs sensors to capture and transmit posture data to the cloud via Wi-Fi. This systematic review examines wearable PMS devices for cervical posture, analysing their attributes, findings, and limitations. Using systematic literature analysis, related studies were collected from diverse databases concentrating on wearable cervical posture devices. The review analysed the outcomes of each neck posture and each monitor type on the CVA ratio based on PMS. However, limitations, such as small sample sizes, limited functions, and privacy concerns were noted across the devices. The findings underscore the importance of considering user comfort and data accuracy in designing and implementing wearable posture monitors. Future studies should also explore the integration of advanced technologies and user-centred design principles to develop more accurate and user-friendly devices.

For accurate diagnosis and prevention of cervical spondylosis, the review focuses on a survey of PMS in the cervical region. Here, research related to wearable monitoring devices regarding the cervical region is investigated. The major finding is the necessity for designing the PMS device with limited sensors, which effectively monitor the cervical region.

[Development of Human Vital Signs and Body Posture Monitoring and Positioning Alarm Systems].

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.

Posture Monitoring and Correction Exercises for Workers in Hostile Environments Utilizing Non-Invasive Sensors: Algorithm Development and Validation.

Personal protective equipment (PPE) is an essential key factor in standardizing safety within the workplace. Harsh working environments with long working hours can cause stress on the human body that may lead to musculoskeletal disorder (MSD). MSD refers to injuries that impact the muscles, nerves, joints, and many other human body areas. Most work-related MSD results from hazardous manual tasks involving repetitive, sustained force, or repetitive movements in awkward postures. This paper presents collaborative research from the School of Electrical Engineering and School of Allied Health at Curtin University. The main objective was to develop a framework for posture correction exercises for workers in hostile environments, utilizing inertial measurement units (IMU). The developed system uses IMUs to record the head, back, and pelvis movements of a healthy participant without MSD and determine the range of motion of each joint. A simulation was developed to analyze the participant's posture to determine whether the posture present would pose an increased risk of MSD with limits to a range of movement set based on the literature. When compared to measurements made by a goniometer, the body movement recorded 94% accuracy and the wrist movement recorded 96% accuracy.

Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training.

INTRODUCTION: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. METHODS: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. RESULTS: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of [Formula: see text]: 0.38 with an error ([Formula: see text]: 12.8). Next, we evaluate its reliability ([Formula: see text] for test-retest), longitudinal external validity ([Formula: see text] true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements ([Formula: see text]: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory ([Formula: see text]: 0.40) and Barthel Index ([Formula: see text]: 0.35). CONCLUSIONS: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

A Two Joint Neck Model to Identify Malposition of the Head Relative to the Thorax.

Neck pain is a frequent health complaint. Prolonged protracted malpositions of the head are associated with neck pain and headaches and could be prevented using biofeedback systems. A practical biofeedback system to detect malpositions should be realized with a simple measurement setup. To achieve this, a simple biomechanical model representing head orientation and translation relative to the thorax is introduced. To identify the parameters of this model, anthropometric data were acquired from eight healthy volunteers. In this work we determine (i) the accuracy of the proposed model when the neck length is known, (ii) the dependency of the neck length on the body height, and (iii) the impact of a wrong neck length on the models accuracy. The resulting model is able to describe the motion of the head with a maximum uncertainty of 5 mm only. To achieve this high accuracy the effective neck length must be known a priory. If however, this parameter is assumed to be a linear function of the palpable neck length, the measurement error increases. Still, the resulting accuracy can be sufficient to identify and monitor a protracted malposition of the head relative to the thorax.

Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution.

Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver's intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures, and three typical right foot postures. The features from lower-resolution mapping were compared with those from high-resolution Xsensor pressure mats on the backrest and seat pan. We applied five different supervised machine-learning techniques to recognize the postures of each body part and used leave-one-out cross-validation to evaluate their performance. A uniform sampling method was used to reduce number of pressure sensors, and five new layouts were tested by using the best classifier. Results showed that the random forest classifier outperformed the other classifiers with an average classification accuracy of 86% using the original pressure mats and 85% when only 8% of the pressure sensors were available. This study demonstrates the feasibility of using fewer pressure sensors for driver posture monitoring and suggests research directions for better sensor designs.

Validation and Assessment of a Posture Measurement System with Magneto-Inertial Measurement Units.

Inappropriate posture and the presence of spinal disorders require specific monitoring systems. In clinical settings, posture evaluation is commonly performed with visual observation, electrogoniometers or motion capture systems (MoCaps). Developing a measurement system that can be easily used also in non-structured environments would be highly beneficial for accurate posture monitoring. This work proposes a system based on three magneto-inertial measurement units (MIMU), placed on the backs of seventeen volunteers on the T3, T12 and S1 vertebrae. The reference system used for validation is a stereophotogrammetric motion capture system. The volunteers performed forward bending and sit-to-stand tests. The measured variables for identifying the posture were the kyphosis and the lordosis angles, as well as the range of movement (ROM) of the body segments. The comparison between MIMU and MoCap provided a maximum RMSE of 5.6° for the kyphosis and the lordosis angles. The average lumbo-pelvic contribution during forward bending (41.8 ± 8.6%) and the average lumbar ROM during sit-to-stand (31.8 ± 9.8° for sitting down, 29.6 ± 7.6° for standing up) obtained with the MIMU system agree with the literature. In conclusion, the MIMU system, which is wearable, inexpensive and easy to set up in non-structured environments, has been demonstrated to be effective in posture evaluation.

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning.

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced.

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.

Motor Control Training for the Shoulder with Smart Garments.

Wearable technologies for posture monitoring and posture correction are emerging as a way to support and enhance physical therapy treatment, e.g., for motor control training in neurological disorders or for treating musculoskeletal disorders, such as shoulder, neck, or lower back pain. Among the various technological options for posture monitoring, wearable systems offer potential advantages regarding mobility, use in different contexts and sustained tracking in daily life. We describe the design of a smart garment named Zishi to monitor compensatory movements and evaluate its applicability for shoulder motor control training in a clinical setting. Five physiotherapists and eight patients with musculoskeletal shoulder pain participated in the study. The attitudes of patients and therapists towards the system were measured using standardized survey instruments. The results indicate that patients and their therapists consider Zishi a credible aid for rehabilitation and patients expect it will help towards their recovery. The system was perceived as highly usable and patients were motivated to train with the system. Future research efforts on the improvement of the customization of feedback location and modality, and on the evaluation of Zishi as support for motor learning in shoulder patients, should be made.

Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model.

Body posture and activity are important indices for assessing health and quality of life, especially for elderly people. Therefore, an easily wearable device or instrumented garment would be valuable for monitoring elderly people's postures and activities to facilitate healthy aging. In particular, such devices should be accepted by elderly people so that they are willing to wear it all the time. This paper presents the design and development of a novel, textile-based, intelligent wearable vest for real-time posture monitoring and emergency warnings. The vest provides a highly portable and low-cost solution that can be used both indoors and outdoors in order to provide long-term care at home, including health promotion, healthy aging assessments, and health abnormality alerts. The usability of the system was verified using a technology acceptance model-based study of 50 elderly people. The results indicated that although elderly people are anxious about some newly developed wearable technologies, they look forward to wearing this instrumented posture-monitoring vest in the future.

Optical fiber sensors for posture monitoring, ulcer detection and control in a wheelchair: a state-of-the-art.

BACKGROUND: In the last ten years, the design and implementation of Optical Fiber Sensors (OFS) in biomedical applications have been discussed, with a focus on different subareas, such as body parameter monitoring and control of assistive devices. MATERIALS AND METHODS: A scoping review was performed including scientific literature (PubMed/Scopus, IEEE and Web of Science), patents (WIPO/Google Scholar), and commercial information. RESULTS: The main applications of OFS in the rehabilitation field for preventing future postural diseases and applying them in device controllers were discussed in this review. Physical characteristics of OFS, different uses, and applications of Polymer Optical Fiber pressure sensors are mentioned. The main postures used for posture monitoring analysis when the user is sitting are normal position, crooked back, high lumbar pressure, sitting on the edge of the chair, and crooked back, left position, and right position. Additionally, it is possible to use Machine Learning (ML) algorithms for posture classification, and device control such as Support Vector Machine, k-Nearest Neighbors, etc., obtaining accuracies above 97%. Moreover, the literature mentions wheelchair controllers and Graphical User Interfaces using pressure maps to provide feedback to the user. CONCLUSIONS: OFS have been used in several healthcare applications as well as postural and preventive applications. The literature showed an effort to implement and design accessible devices for people with disabilities and people with specific diseases. Alternatively, ML algorithms are widely used in this direction, leaving the door open for further studies that allow the application of real-time systems for posture monitoring and wheelchairs control.

IMPLICATIONS FOR REHABILITATIONPosture monitoring and ulcer detection systems are very useful to prevent or treat diseases related to bedsores or pressure ulcers using a different kind of electronic or optic instrumentation to improve the user's quality of life.The system characteristics using optical fiber sensors discussed in this review set an important precedent in the fabrication of low-cost systems for biomedical applications.

Use of a Multi-Sensor Monitoring Device in an Early Post-operative Mobilization Program.

BACKGROUND: The current study aimed to evaluate the validity and feasibility of using a multi-sensor device to monitor patient mobility in a large postoperative population. METHODS: In this IRB-approved study, postoperative patient posture was recorded using a multi-sensor monitoring device (ViSi Mobile®) and compared with direct observations of patient physical activity. Retrospective cohort analysis of postoperative patient posture data from January to December 2019 was then performed. Patterns of postoperative mobilization were evaluated. RESULTS: Multi-sensor real-time posture monitoring with the ViSi Mobile® system consistently differentiate between rest and upright posture (sensitivity and specificity, both 100%). During observation of ambulatory events, ViSi Mobile® system correctly recorded a patient's position as upright at each validation time point in 72.7% (8 of 11) of walks. Clinical data from 562 postoperative patients were linked with posture monitoring data. Median duration of posture monitoring was 64 hours (IQR 52.5) and median number of posture positions recorded per patient was 15,370 (IQR 12,685). Median duration of upright position per day was 148.6 minutes (IQR 192.8). Duration in active upright position per day was not associated with risk of readmission (P > .05). CONCLUSION: Real-time posture data from a multi-sensor monitoring device (ViSi Mobile®) was shown to consistently differentiate rest and active upright position. This novel technology can provide useful insight into adherence and clinical benefit of early mobilization programs.

Lumbopelvic postural differences in adolescent idiopathic scoliosis: A pilot study.

BACKGROUND: A primary etiology of adolescent idiopathic scoliosis (AIS) is currently unknown, but poor postural control of the spinal extensor musculature has been identified as an AIS risk factor. Identifiable postural differences would aid in advancing the precise postural behaviors that should be modified during Physiotherapy Scoliosis Specific Exercise (PSSE) to help limit the progression of AIS. RESEARCH QUESTION: Are there any determinable differences in lumbopelvic posture or range of motion between subjects with AIS and controls? METHODS: This prospective cohort pilot study consisted of 53 subjects (27 AIS and 26 control) aged 11-17 years. Subjects had their lumbopelvic posture assessed and monitored using the ViMove DorsaVi sensor package. All subjects underwent a live assessment to obtain initial lumbopelvic (LP) range of motion (ROM) measurements. Subjects were then monitored while continuing with normal activities of daily living (ADLs) for 12 h. With an alpha level of 0.05, nonparametric analyses were performed for each variable via a Mann-Whitney U-test. RESULTS: During the live assessment, controls exhibited a significantly greater anterior pelvic tilt ROM in the sitting position than the AIS group (p = 0.0433). When compared to female controls, females with AIS had a sitting pelvic tilt ROM that was significantly more retroverted (p = 0.0232) and less anteverted (p = 0.0010). During ADLs, female controls exhibited a higher total number of extension events than their female with AIS (p = 0.0263). These associations did not strengthen with greater spinal deformity. SIGNIFICANCE: This work demonstrates postural differences between patients with AIS and controls. Further study is necessary to determine why patients with AIS adopt these postures, and if PSSEs can be utilized to limit the progression of AIS.

Driver posture monitoring in highly automated vehicles using pressure measurement.

OBJECTIVE: Driver posture monitoring is useful for evaluating the readiness to take over from highly automated driving systems as well as for designing intelligent restraint systems to reduce injury. The aim of this study was to develop a real-time and robust driver posture monitoring system using pressure measurement. METHODS: Driver motion and pressure measurement were collected from 23 differently sized participants performing 42 driving and non-driving activities. Nine typical driver postures were identified by analyzing trunk and feet positions in 3 D space for classification. One deep learning classifier and two Random Forest classifiers were trained respectively on pressure distribution, absolute and relative pressure features extracted from pressure measurement. Leave-One-Out cross-validation was performed to evaluate the performance of the classifiers. RESULTS: Without considering feet positions, all the classifiers could provide reliable recognition of the normal trunk position for standard driving with an accuracy around 98%. With help of a reference sitting position, the best performance was achieved by Random Forest classifier trained on the relative pressure features with an average classification accuracy of 80.5% across 9 typical postures and 23 drivers. The main errors were related to the recognition of feet positions when applying braking and relaxing both feet on the floor. CONCLUSIONS: Pressure measurement could be a good alternative or complementary to camera based driver postural monitoring system. Results show that all classifiers proposed in the work could predict the trunk position for standard driving. With help of an initial posture, Random Forest classifier with relative pressure features could classify trunk positions with high accuracy. However, further effort is needed to improve the accuracy of feet position prediction especially by adding more foot related task data.

Evaluation of spinal posture during gait with inertial measurement units.

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt-twist method was adopted to evaluate flexion-extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context.

Low cost self-made pressure distribution sensors for ergonomic chair: Are they suitable for posture monitoring?

BACKGROUND: Long sitting causes many health problems for people. Healthy sitting monitoring systems, like real-time pressure distribution measuring, is in high demand and many methods of posture recognition were developed. Such systems are usually expensive and hardly available for the regular user. OBJECTIVE: The aim of study is to develop low cost but sensitive enough pressure sensors and posture monitoring system. METHODS: New self-made pressure sensors have been developed and tested, and prototype of pressure distribution measuring system was designed. RESULTS: Sensors measured at average noise amplitude of a = 56 mV (1.12%), average variation in sequential measurements of the same sensor s = 17 mV (0.34%). Signal variability between sensors averaged at 100 mV (2.0%). Weight to signal dependency graph was measured and hysteresis calculated. Results suggested the use of total sixteen sensors for posture monitoring system with accuracy of < 1.5% after relaxation and repeatability of around 2%. CONCLUSION: Results demonstrate that hand-made sensor sensitivity and repeatability are acceptable for posture monitoring, and it is possible to build low cost pressure distribution measurement system with graphical visualization without expensive equipment or complicated software.

Development of Human Vital Signs and Body Posture Monitoring and Positioning Alarm Systems / 中国医疗器械杂志

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.