Measurement and Geometric Modelling of Human Spine Posture for Medical Rehabilitation Purposes Using a Wearable Monitoring System Based on Inertial Sensors

Multimodal video and IMU kinematic dataset on daily life activities using affordable devices

Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient gross motor tracking solutions for daily life activities recognition and kinematic analysis. The dataset includes 13 activities registered using a commodity camera and five inertial sensors. The video recordings were acquired in 54 subjects, of which 16 also had simultaneous recordings of inertial sensors. The novelty of dataset lies in: (i) the clinical relevance of the chosen movements, (ii) the combined utilization of affordable video and custom sensors, and (iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction in a musculoskeletal model from inertial data. The validation confirms that a minimally disturbing acquisition protocol, performed according to real-life conditions can provide a comprehensive picture of human joint angles during daily life activities.

Mobility Analysis of the Lumbar Spine with a Dynamic Spine-Correction Device

According to data, 60-70% of the world's population experience low-back pain (LBP) at least once during their lifetime, often at a young or middle age. Those affected are at risk of having worse quality of life, more missed days at work, and higher medical care costs. We present a new rehabilitation method that helps collect and analyze data on an ongoing basis and offers a more personalized therapeutic approach. This method involves assessing lumbar spine rotation (L1-L5) during torso movement using an innovative dynamic spine correction (DSC) device designed for postural neuromuscular reeducation in LBP. Spinal mobility was tested in 54 patients (aged 18 to 40 years) without LBP. Measurements were made with 12-bit rotary position sensors (AS5304) of the DSC device. During exercise, the mean lumbar spine rotation to the right was greater (4.78° ± 2.24°) than that to the left (2.99° ± 1.44°; p < 0.001). Similarly, the maximum rotation to the right was greater (11.35° ± 3.33°) than that to the left (7.42° ± 1.44°; p < 0.0001). The measurements obtained in the study can serve as a reference for future therapeutic use of the device.

Reliability in measurement of three-dimensional anterior pelvic plane orientation by registration with an inertial measurement unit

It is strongly challenging to obtain functional movement of the pelvis based on the three-dimensional (3D) dynamic anterior pelvic plane (APP) orientation information. This study provided the 3D APP orientation measurement technique by registration with an inertial measurement unit (IMU), and its reliability was tested. The local coordinate systems of the APP and the IMU sensor were registered using two images of the pelvic part from the frontal and left sagittal views in a neutral standing posture. Then, the measurement errors in the APP orientation were analyzed by comparing the values obtained from manually measured four points in the IMU sensor and the known exact values in 10 different postures. Moreover, the errors between values obtained from manually measured three anatomical points and the known exact values were also compared. The average errors were quite small (less than 0.6°) when measuring from three anatomical points and were acceptable (1.6°–3.4°) when measuring from four points in the IMU sensor. These results indicate that the measurement of APP direction using four points in the IMU sensor could be considered reliable in terms of intra-participant and inter-participant. The present technique to register the IMU sensor position and the APP direction by taking X-ray images from the frontal and sagittal directions can be fundamental information to measure the APP direction during dynamic motion when the IMU position is obtained from the IMU sensor data instead of the four-point location information.

Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey

Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, i.e., using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.

Determination of the 3D Human Spine Posture from Wearable Inertial Sensors and a Multibody Model of the Spine

Determination of spine posture is of great interest for the effective prevention, evaluation, treatment and evolution monitoring of spinal disorders. Limitations of traditional imaging systems, including cost, radiation exposure (for X-ray based systems), projection volume issues and subject positioning requirements, etc., make non-invasive motion assessment tools effective alternatives for clinical and non-clinical use. In this work, a procedure was developed to obtain a subject-specific multibody model of the spine using either inertial or optical sensors and, based on this multibody model, to estimate the locations and orientations of the 17 vertebrae constituting the thoracolumbar spine. The number and calibration of the sensors, angular offsets, scaling difficulties and gender differences were addressed to achieve an accurate 3D-representation of the spine. The approach was validated by comparing the estimated positions of the sensors on 14 healthy subjects with those provided by an optical motion capture system. A mean position error of lower than 12 mm was obtained, thus showing that the proposed method can offer an effective non-invasive tool for the assessment of spine posture.

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.

Real-Time Back Surface Landmark Determination Using a Time-of-Flight Camera

Postural disorders, their prevention, and therapies are still growing modern problems. The currently used diagnostic methods are questionable due to the exposure to side effects (radiological methods) as well as being time-consuming and subjective (manual methods). Although the computer-aided diagnosis of posture disorders is well developed, there is still the need to improve existing solutions, search for new measurement methods, and create new algorithms for data processing. Based on point clouds from a Time-of-Flight camera, the presented method allows a non-contact, real-time detection of anatomical landmarks on the subject’s back and, thus, an objective determination of trunk surface metrics. Based on a comparison of the obtained results with the evaluation of three independent experts, the accuracy of the obtained results was confirmed. The average distance between the expert indications and method results for all landmarks was 27.73 mm. A direct comparison showed that the compared differences were statically significantly different; however, the effect was negligible. Compared with other automatic anatomical landmark detection methods, ours has a similar accuracy with the possibility of real-time analysis. The advantages of the presented method are non-invasiveness, non-contact, and the possibility of continuous observation, also during exercise. The proposed solution is another step in the general trend of objectivization in physiotherapeutic diagnostics.

Ranging with Frequency Dependent Ultrasound Air Attenuation

Measuring the distance between two points has multiple uses. Position can be geometrically calculated from multiple measurements of the distance between reference points and moving sensors. Distance measurement can be done by measuring the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. However, this requires close synchronization between the emitter and the sensors. This synchronization is usually done using a radio or optical channel, which requires additional hardware and power to operate. On the other hand, for many applications of great interest, low-cost, small, and lightweight sensors with very small batteries are required. Here, an innovative technique to measure the distance between emitter and receiver by using ultrasonic signals in air is proposed. In fact, the amount of the signal attenuation in air depends on the frequency content of the signal itself. The attenuation level that the signal undergoes at different frequencies provides information on the distance between emitter and receiver without the need for any synchronization between them. A mathematical relationship here proposed allows for estimating the distance between emitter and receiver starting from the measurement of the frequency dependent attenuation along the traveled path. The level of attenuation in the air is measured online along the operation of the proposed technique. The simulations showed that the range accuracy increases with the decrease of the ultrasonic transducer diameter. In particular, with a diameter of 0.5 mm, an error of less than ± 2.7 cm (average value 1.1 cm) is reached along two plane sections of the typical room of the office considered (4 × 4 × 3 m3).

The past, present, and future of remote patient monitoring in spine care: an overview

PURPOSE

Remote patient monitoring (RPM) has revolutionized the landscape of healthcare. From humble beginnings rooted in landline home telephone calls to present-day devices with near instantaneous wireless connectivity, the evolution of technology has ushered in an era of digital medicine and remote care. Presently, a vast array of healthcare data points can be automatically generated, analyzed, and forwarded to providers to supplement clinical decision-making. While RPM originated and was popularized within medicine, its role in orthopedics, and particularly within spine surgery, is evolving. We sought to provide an overview of RPM within orthopedics, with specific attention on spine care, analyzing its origins, present-day form, and prospects.

METHODS

We reviewed the literature to date as it pertains to RPM within healthcare at large, orthopedics, and spine care.

RESULTS

We detail the development and clinical use of wearable technology and smart implants, examining the underlying technology and evaluating the spectrum of their present-day and potential applications.

CONCLUSIONS

Technological advancements are not only reshaping the paradigm of musculoskeletal care but are also redefining the physician-patient relationship as well as reimagining traditional perspectives on healthcare data collection and privacy.

Spine Cop: Posture Correction Monitor and Assistant

Back and spine-related issues are frequent maladies that most people have or will experience during their lifetime. A common and sensible observation that can be made is regarding the posture of an individual. We present a new approach that combines accelerometer, gyroscope, and magnetometer sensor data in combination with permanent magnets assembled as a wearable device capable of real-time spine posture monitoring. An independent calibration of the device is required for each user. The sensor data is processed by a probabilistic classification algorithm that compares the real-time data with the calibration result, verifying whether the data point lies within regions of confidence defined by a computed threshold. An incorrect posture classification is considered if both accelerometer and magnetometer classify the posture as incorrect. A pilot trial was performed in a single adult test subject. The combination of the magnets and magnetometer greatly improved the posture classification accuracy (89%) over the accuracy obtained when only accelerometer data were used (47%). The validation of this method was based on image analysis.

Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems

Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints’ geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.

Wearable Technology in Spine Surgery

Although patient-reported outcome measures (PROMs) provide valuable insight into the effectiveness of spine surgery, there still remain limitations on measuring outcomes in this manner. Among other deficiencies, PROMs do not always correlate with more objective measures of surgery success. Wearable technology, such as pedometers, tri-axis accelerometer, or wearable cameras, may allow physicians to track patient progress following spine surgery more objectively. Recently, there has been an emphasis on using wearable devices to measure physical activity and limb and spine function. Wearable devices could play an important role as a supplement to PROMs, although they might have to be substantiated through adequate controlled studies to identify normative data for patients presenting with common spine disorders. This review will detail the current state of wearable technology applications in spine surgery and its direction as its utilization expands.

A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively.

Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device

Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.

Highly Sensitive and Durable Structured Fibre Sensors for Low-Pressure Measurement in Smart Skin

Precise measurements of low pressure are highly necessary for many applications. This study developed novel structured fibre sensors embedded in silicone, forming smart skin with high sensitivity, high durability, and good immunity to crosstalk for precise measurement of pressure below 10 kPa. The transduction principle is that an applied pressure leads to bending and stretching of silicone and optical fibre over a purposely made groove and induces the axial strain in the gratings. The fabricated sensor showed high pressure sensitivity up to 26.8 pm/kPa and experienced over 1,000,000 cycles compression without obvious variation. A theoretical model of the sensor was presented and verified to have excellent agreement with experimental results. The prototype of smart leg mannequin and wrist pulse measurements indicated that such optical sensors can precisely measure low-pressure and can easily be integrated for smart skins for mapping low pressure on three-dimensional surfaces.

The role of wearables in spinal posture analysis: a systematic review

Background

Wearables consist of numerous technologies that are worn on the body and measure parameters such as step count, distance travelled, heart rate and sleep quantity. Recently, various wearable systems have been designed capable of detecting spinal posture and providing live biofeedback when poor posture is sustained. It is hypothesised that long-term use of these wearables may improve spinal posture.

Research questions

To (1) examine the capabilities of current devices assessing spine posture, (2) to identify studies implementing such devices in the clinical setting and (3) comment on the clinical practicality of integration of such devices into routine care where appropriate.

Methods

A comprehensive systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) across the following databases: PubMed; MEDLINE; EMBASE; Cochrane; and Scopus. Articles related to wearables systems able to measure spinal posture were selected amongst all published studies dated from 1980 onwards. Extracted data was collected as per a predetermined checklist including device types, study objectives, findings and limitations.

Results

A total of 37 articles were extensively reviewed and analysed in the final review. The proposed wearables most commonly used Inertial Measurement Units (IMUs) as the underlying technology. Wearables measuring spinal posture have been proposed to be used in the following settings: post-operative rehabilitation; treatment of musculoskeletal disorders; diagnosis of pathological spinal posture; monitoring of progression of Parkinson’s Disease; detection of falls; workplace occupational health and safety; comparison of interventions.

Conclusions

This is the first and only study to specifically review wearable devices that monitor spinal posture. Our findings suggest that currently available devices are capable of assessing spinal posture with good accuracy in the clinical setting. However, further validation regarding the long-term use of these technologies and improvements regarding practicality is required for commercialisation.

Alterations of Muscle Synergies During Voluntary Arm Reaching Movement in Subacute Stroke Survivors at Different Levels of Impairment

Motor system uses muscle synergies as a modular organization to simplify the control of movements. Motor cortical impairments, such as stroke and spinal cord injuries, disrupt the orchestration of the muscle synergies and result in abnormal movements. In this paper, the alterations of muscle synergies in subacute stroke survivors were examined during the voluntary reaching movement. We collected electromyographic (EMG) data from 35 stroke survivors, ranging from Brunnstrom Stage III to VI, and 25 age-matched control subjects. Muscle synergies were extracted from the activity of 7 upper-limb muscles via nonnegative matrix factorization under the criterion of 95% variance accounted for. By comparing the structure of muscle synergies and the similarity of activation coefficients across groups, we can validate the increasing activation of pectoralis major muscle and the decreasing activation of elbow extensor of triceps in stroke groups. Furthermore, the similarity of muscle synergies was significantly correlated with the Brunnstrom Stage (R = 0.52, p < 0.01). The synergies of stroke survivors at Brunnstrom Stage IV–III gradually diverged from those of control group, but the activation coefficients remained the same after stroke, irrespective of the recovery level.

Gait Shear and Plantar Pressure Monitoring: A Non-Invasive OFS Based Solution for e-Health Architectures

In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this field, we propose an optical fiber sensor (OFS) based system for the simultaneous monitoring of shear and plantar pressure during gait movement. These parameters are considered to be two key factors in gait analysis that can help in the early diagnosis of multiple anomalies, such as diabetic foot ulcerations or in physical rehabilitation scenarios. The proposed solution is a biaxial OFS based on two in-line fiber Bragg gratings (FBGs), which were inscribed in the same optical fiber and placed individually in two adjacent cavities, forming a small sensing cell. Such design presents a more compact and resilient solution with higher accuracy when compared to the existing electronic systems. The implementation of the proposed elements into an insole is also described, showcasing the compactness of the sensing cells, which can easily be integrated into a non-invasive mobile e-Health solution for continuous remote gait monitoring of patients and elder citizens. The reported results show that the proposed system outperforms existing solutions, in the sense that it is able to dynamically discriminate shear and plantar pressure during gait.

Development of a 3D workspace shoulder assessment tool incorporating electromyography and an inertial measurement unit-a preliminary study

Traditional shoulder range of movement (ROM) measurement tools suffer from inaccuracy or from long experimental setup times. Recently, it has been demonstrated that relatively low-cost wearable inertial measurement unit (IMU) sensors can overcome many of the limitations of traditional motion tracking systems. The aim of this study is to develop and evaluate a single IMU combined with an electromyography (EMG) sensor to monitor the 3D reachable workspace with simultaneous measurement of deltoid muscle activity across the shoulder ROM. Six volunteer subjects with healthy shoulders and one participant with a ‘frozen’ shoulder were recruited to the study. Arm movement in 3D space was plotted in spherical coordinates while the relative EMG intensity of any arm position is presented graphically. The results showed that there was an average ROM surface area of 27291 ± 538 deg² among all six healthy individuals and a ROM surface area of 13571 ± 308 deg² for the subject with frozen shoulder. All three sections of the deltoid show greater EMG activity at higher elevation angles. Using such tools enables individuals, surgeons and physiotherapists to measure the maximum envelope of motion in conjunction with muscle activity in order to provide an objective assessment of shoulder performance in the voluntary 3D workspace.

Graphical abstract