Commercial and research-based wearable devices in spinal postural analysis: A systematic review

The widespread use of ubiquitous computing has led to people spending more time in front of screens, causing poor posture. The COVID-19 pandemic and the shift towards remote work have only worsened the situation, as many people are now working from home with inadequate ergonomics. Maintaining a healthy posture is crucial for both physical and mental health, and poor posture can result in spinal problems. Wearable systems have been developed to monitor posture and provide instant feedback. Their goal is to improve posture over time by using these devices. This article will review commercially available, and research-based wearable devices used to analyse posture. The potential of these devices in the healthcare industry, particularly in preventing, monitoring, and treating spinal and musculoskeletal conditions, will also be discussed. The findings indicate that current devices can accurately assess posture in clinical settings, but further research is needed to validate the long-term effectiveness of these technologies and to improve their practicality for commercial use

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

Wearable inertial sensors and range of motion metrics in physical therapy remote support

Abstract. The practice of physiotherapy diagnoses patient ailments which are often treated by the daily repetition of prescribed physiotherapeutic exercise. The effectiveness of the exercise regime is dependent on regular daily repetition of the regime and the correct execution of the prescribed exercises. Patients often have issues learning unfamiliar exercises and performing the exercise with good technique. This design science research study examines a back squat classifier design to appraise patient exercise regime away from the physiotherapy practice. The scope of the exercise appraisal is limited to one exercise, the back squat. Kinematic data captured with commercial inertial sensors is presented to a small group of physiotherapists to illustrate the potential of the technology to measure range of motion (ROM) for back squat appraisal. Opinions are considered from two fields of physiotherapy, general musculoskeletal and post-operative rehabilitation. While the exercise classifier is considered not suitable for post-operative rehabilitation, the opinions expressed for use in general musculoskeletal physiotherapy are positive. Kinematic data captured with gyroscope sensors in the sagittal plane is analysed with Matlab to develop a method for back squat exercise recognition and appraisal. The artefact, a back squat classifier with appraisal features is constructed from Matlab scripts which are proven to be effective with kinematic data from a novice athlete

Quantitative Shape Measurement of An Inflatable Rubber Dam Using Inertial Sensors

Shape measurement is of great importance for the effective control and safe operation of inflatable rubber dams. This paper presents for the first time a method to measure the cross-sectional shape of a rubber dam by placing an array of inertial measurement units (IMUs) on the peripheral of the rubber dam. The IMU array measures tangent angles of the dam peripheral by fusing accelerometer and gyroscope measurements. A continuous tangent angle function is derived by interpolating the tangent angles at discrete locations using a cubic spline. Finally, the shape is reconstructed by integrating the tangent angle function along the peripheral of the rubber dam. The performance of the measurement system is validated against a camera on a purpose-built test rig. Experimental results show that the measured and reference shapes are very similar, with a maximum similarity index of 8.5% under typical conditions. In addition, it is demonstrated that the system is robust against node failure by excluding readings of faulty nodes from shape reconstruction

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Comparison of knee loading during walking via musculoskeletal modelling using marker-based and IMU-based approaches

openThe current thesis is the result of the candidate's work over a six-month period with the assistance of the supervisor and co-supervisors, thanks to the collaboration between the Human Movement Bioengineering Laboratory Research group at the University of Padova (Italy) and the Human Movement Biomechanics Research group at KU Leuven (Belgium). Gait analysis, at a clinical level, is a diagnostic test with multiple potentials, in particular in identifying functional limitations related to a pathological path. Three-dimensional motion capture is now consolidated as an approach for human movement research studies and consists of a set of very precise measurements, the latter are processed by biomechanical models, and curves relating to the kinematics and indirect dynamics, i.e., the joint angles and the relative forces and moments, can be obtained. These results are considered fully reliable and based on these curves it is decided how to intervene on the specific subject to make the path as less pathological as possible. However, the use of wearable sensors (IMUs) consisting of accelerometers, gyroscopes, and magnetic sensors for gait analysis, has increased in the last decade due to the low production costs, portability, and small size that have allowed for studies in everyday life conditions. Inertial capture (InCap) systems have become an appealing alternative to 3D Motion Capture (MoCap) systems due to the ability of inertial measurement units (IMUs) to estimate the orientation of 3D sensors and segments. Musculoskeletal modelling and simulation provide the ideal framework to examine quantities in silico that cannot be measured in vivo, such as musculoskeletal loading, muscle forces and joint contact forces. The specific software used in this study is Opensim: an open-source software that allows modelling, analysis, and simulation of the musculoskeletal system. The aim of this thesis is to compare a marker-based musculoskeletal modelling approach with an IMUs-based one, in terms of kinematics, dynamics, and muscle activations. In particular, the project will focus on knee loading, using an existing musculoskeletal model of the lower limb. The current project was organized as follows: first, the results for the MoCap approach were obtained, following a specific workflow that used the COMAK IK tool and the COMAK algorithm to get the secondary knee kinematics, muscle activations, and knee contact forces. Where COMAK is a modified static optimization algorithm that solves for muscle activations and secondary kinematics to obtain measured primary DOF accelerations while minimizing muscle activation. Then these results were used to make a comparison with those obtained by the inertial-based approach, with the attempt to use as little information as possible from markers while estimating kinematics from IMU data using an OpenSim toolbox called OpenSense. Afterward, in order to promote an approach more independent from the constraints of a laboratory, the Zero Moment Point (ZMP) method was used to estimate the center of pressure position of the measured ground reaction forces (GRFs), and a specific Matlab code was implemented to improve this estimation. Using the measured GRFs with the new CoPs, the results of Inverse Dynamics, muscle activations, and finally knee loading were calculated and compared to the MoCap results. The final step was to conduct a statistical analysis to compare the two approaches and emphasize the importance of using IMUs for gait analysis, particularly to study knee mechanics

A Framework for Gamification of Human Joint Remote Rehabilitation, Incorporating Non-Invasive Sensors

Patients who have suffered soft tissue injuries or undergone surgery often experience reduced muscle strength, flexibility, and pain in the affected area, which can interfere with daily activities. Rehabilitation exercises are crucial in reducing symptoms and returning patients to normal activities. This research presents a framework for human joint rehabilitation that enables clinicians to set engaging gamified rehabilitation tasks for their patients utilising non-invasive sensors and machine learning algorithms

How to Make Fabrics Talk Environment: The Scatol8 per la Sostenibilità Way

The mission of Scatol8 per la Sostenibilità srl (in English Scatol8 for Sustainability) is the realization of products and services to communicate the theme of sustainable development. The economic viability of the entrepreneurial initiative is ensured by three lines of activity: (1) the realization of personalized management systems, (2) carrying out environmental education activities, and (3) design and manufacture of products. Scatol8’s products are unique design objects—therefore exclusive—crafted with materials and techniques derived from scientific research. Osmosis between new researches in the fields of business management, chemistry, environment, and the Scatol8 company leads to the manufacture of products that incorporate recent innovations and transfer the knowledge content to those who can afford them, meaning those who have culture enough to understand and appreciate them. It has been said: luxury is experiential. This is the concept we aim for with our products. The following chapter describes the qualities of some sustainable luxury products and deepens the ways of thinking, designing, and making a line of garments. These garments incorporate electronic devices with natural fabrics: cotton, hemp, and eco-leather. This is an example of upcycling as a result of technological innovation, which is then analyzed by the design thinking

Fusion of wearable and visual sensors for human motion analysis

Human motion analysis is concerned with the study of human activity recognition, human motion tracking, and the analysis of human biomechanics. Human motion analysis has applications within areas of entertainment, sports, and healthcare. For example, activity recognition, which aims to understand and identify different tasks from motion can be applied to create records of staff activity in the operating theatre at a hospital; motion tracking is already employed in some games to provide an improved user interaction experience and can be used to study how medical staff interact in the operating theatre; and human biomechanics, which is the study of the structure and function of the human body, can be used to better understand athlete performance, pathologies in certain patients, and assess the surgical skill of medical staff. As health services strive to improve the quality of patient care and meet the growing demands required to care for expanding populations around the world, solutions that can improve patient care, diagnosis of pathology, and the monitoring and training of medical staff are necessary. Surgical workflow analysis, for example, aims to assess and optimise surgical protocols in the operating theatre by evaluating the tasks that staff perform and measurable outcomes. Human motion analysis methods can be used to quantify the activities and performance of staff for surgical workflow analysis; however, a number of challenges must be overcome before routine motion capture of staff in an operating theatre becomes feasible. Current commercial human motion capture technologies have demonstrated that they are capable of acquiring human movement with sub-centimetre accuracy; however, the complicated setup procedures, size, and embodiment of current systems make them cumbersome and unsuited for routine deployment within an operating theatre. Recent advances in pervasive sensing have resulted in camera systems that can detect and analyse human motion, and small wear- able sensors that can measure a variety of parameters from the human body, such as heart rate, fatigue, balance, and motion. The work in this thesis investigates different methods that enable human motion to be more easily, reliably, and accurately captured through ambient and wearable sensor technologies to address some of the main challenges that have limited the use of motion capture technologies in certain areas of study. Sensor embodiment and accuracy of activity recognition is one of the challenges that affect the adoption of wearable devices for monitoring human activity. Using a single inertial sensor, which captures the movement of the subject, a variety of motion characteristics can be measured. For patients, wearable inertial sensors can be used in long-term activity monitoring to better understand the condition of the patient and potentially identify deviations from normal activity. For medical staff, inertial sensors can be used to capture tasks being performed for automated workflow analysis, which is useful for staff training, optimisation of existing processes, and early indications of complications within clinical procedures. Feature extraction and classification methods are introduced in thesis that demonstrate motion classification accuracies of over 90% for five different classes of walking motion using a single ear-worn sensor. To capture human body posture, current capture systems generally require a large number of sensors or reflective reference markers to be worn on the body, which presents a challenge for many applications, such as monitoring human motion in the operating theatre, as they may restrict natural movements and make setup complex and time consuming. To address this, a method is proposed, which uses a regression method to estimate motion using a subset of fewer wearable inertial sensors. This method is demonstrated using three sensors on the upper body and is shown to achieve mean estimation accuracies as low as 1.6cm, 1.1cm, and 1.4cm for the hand, elbow, and shoulders, respectively, when compared with the gold standard optical motion capture system. Using a subset of three sensors, mean errors for hand position reach 15.5cm. Unlike human motion capture systems that rely on vision and reflective reference point markers, commonly known as marker-based optical motion capture, wearable inertial sensors are prone to inaccuracies resulting from an accumulation of inaccurate measurements, which becomes increasingly prevalent over time. Two methods are introduced in this thesis, which aim to solve this challenge using visual rectification of the assumed state of the subject. Using a ceiling-mounted camera, a human detection and human motion tracking method is introduced to improve the average mean accuracy of tracking to within 5.8cm in a laboratory of 3m × 5m. To improve the accuracy of capturing the position of body parts and posture for human biomechanics, a camera is also utilised to track the body part movements and provide visual rectification of human pose estimates from inertial sensing. For most subjects, deviations of less than 10% from the ground truth are achieved for hand positions, which exhibit the greatest error, and the occurrence of sources of other common visual and inertial estimation errors, such as measurement noise, visual occlusion, and sensor calibration are shown to be reduced.Open Acces