Human Motion Analysis with Wearable Inertial Sensors

High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system

Optimal 3D arm strategies for maximizing twist rotation during somersault of a rigid-body model

Looking for new arm strategies for better twisting performances during a backward somersault is of interest for the acrobatic sports community while being a complex mechanical problem due to the nonlinearity of the dynamics involved. As the pursued solutions are not intuitive, computer simulation is a relevant tool to explore a wider variety of techniques. Simulations of twisting somersaults have mainly been realized with planar arm motions. The aim of this study was to explore the outcomes of using 3D techniques, with the demonstration that increasing the fidelity of the model does not increase the level of control complexity on the real system. Optimal control was used to maximize twists in a backward straight somersault with both types of models. A multistart approach was used to find large sets of near-optimal solutions. The robustness of these solutions was then assessed by modeling kinematic noise during motion execution. The possibility of using quaternions for representing orientations in this numerical optimization problem was discussed. Optimized solutions showed that 3D techniques generated about two additional twists compared to 2D techniques. The robustness analysis revealed clusters of highly twisting and stable 3D solutions. This study demonstrates the superiority of 3D solutions for twisting in backward somersault, a result that can help acrobatic sports athletes to improve their twisting performance

Real-Time Human Motion Capture Driven by a Wireless Sensor Network

The motion of a real object model is reconstructed through measurements of the position, direction, and angle of moving objects in 3D space in a process called “motion capture.” With the development of inertial sensing technology, motion capture systems that are based on inertial sensing have become a research hot spot. However, the solution of motion attitude remains a challenge that restricts the rapid development of motion capture systems. In this study, a human motion capture system based on inertial sensors is developed, and the real-time movement of a human model controlled by real people’s movement is achieved. According to the features of the system of human motion capture and reappearance, a hierarchical modeling approach based on a 3D human body model is proposed. The method collects articular movement data on the basis of rigid body dynamics through a miniature sensor network, controls the human skeleton model, and reproduces human posture according to the features of human articular movement. Finally, the feasibility of the system is validated by testing of system properties via capture of continuous dynamic movement. Experiment results show that the scheme utilizes a real-time sensor network-driven human skeleton model to achieve the accurate reproduction of human motion state. The system also has good application value

The use of inertial measurement units for the determination of gait spatio-temporal parameters

The aim of this work was to develop a methodology whereby inertial measurement units (IMUs) could be used to obtain accurate and objective gait parameters within typical developed adults (TDA) and Parkinson’s disease (PD). The thesis comprised four studies, the first establishing the validity of the IMU method when measuring the vertical centre of mass (CoM) acceleration, velocity and position versus an optical motion capture system (OMCS) in TDA. The second study addressed the validity of the IMU and inverted pendulum model measurements within PD and also explored the inter-rater reliability of the measurement. In the third study the optimisation of the inverted pendulum model driven by IMU data was explored when comparing to standardised clinical tests within TDA and PD, and the fourth explored a novel phase plot analysis applied to CoM movement to explore gait in more detail. The validity study showed no significant difference for vertical acceleration and position between IMU and OMCS measurements within TDA. Vertical velocity however did show a significant difference, but the error was still less than 2.5%. ICCs for all three parameters ranged from 0.782 to 0.952, indicating an adequate test-retest reliability. Within PD there was no significant difference found for vertical CoM acceleration, velocity and position. ICCs for all three parameters ranged from 0.77 to 0.982. In addition, the reliability calculations found no difference for step time, stride length and walking speed for people with PD. Inter-rater reliability was found not to be different for the same parameters. The optimisation of the correction factor when using the inverted pendulum model showed no significant difference between TDA and PD. Furthermore the correction factor was found not to be related to walking speed. The fourth and final study found that phase plot analysis of variability could be performed on CoM vertical excursion. TDA and PD were shown to have, on average, different characteristics. This thesis demonstrated that CoM motion can be objectively measured within a clinical setting in people with PD by utilizing IMUs. Furthermore, in depth gait variability analysis can be performed by utilizing a phase plot method

Quantifying the ergonomic risk and biomechanical exposure in automotive assembly lines

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasAs Lesões Músculo-esqueléticas Relacionadas com o Trabalho (LMERTs) representam 15% do número total de anos de vida perdidos por danos físicos ou doenças com a sua génese no trabalho. De entre os fatores de risco para as LMERTs, no presente estudo, destacam-se as posturas corporais relacionadas com o trabalho. A exposição biomecânica a posturas consideradas prejudiciais tem um impacto negativo na saúde dos trabalhadores, na economia das empresas e na sociedade. A fim de aperceber a prática recorrente de posturas prejudiciais no local de trabalho, têm sido invocados métodos de autoavaliação ergonómica, nos quais o risco é percecionado pelo próprio trabalhador; observacionais, conduzidos por peritos em ergonomia; e de medição direta, que recorrem ao emprego de soluções tecnológicas para a recolha e monitorização objetiva de variáveis pertinentes para a avaliação ergonómica. Porém, frequentemente e em contexto industrial, são apenas aplicados métodos de autoavaliação e observacionais, apesar da medição direta constituir uma solução mais notável. O advento da Internet das Coisas vem revelar a oportunidade da utilização de wearables para uma recolha de dados omnipresente, amplificando a quantidade de dados disponível com o fim de uma avaliação ergonómica mais individual e imparcial. Deste modo, estudos relativos à avaliação ergonómica no local de trabalho têm primado pelo uso de wearables com vista a monitorização do movimento humano. A presente dissertação respeita ao desenvolvimento de uma abordagem automática para a avaliação ergonómica em contexto industrial. As contribuições principais são o desenvolvimento de (1) uma rotina de captura de movimento, através da utilização de um sistema wearable com sensores inerciais; (2) uma framework computacional para a monitorização do movimento da parte superior do corpo humano, em termos dos ângulos relativos às articulações entre os segmentos anatómicos, estimados com recurso à cinemática inversa; e (3) implementações computacionais de especificações estabelecidas e relativas aos fatores de risco de postura para a quantificação da exposição biomecânica e consequente risco ergonómico em âmbito ocupacional. Subsequentemente, as implementações das especificações foram aplicadas por forma a prover constatações acerca de um caso de estudo das linhas de montagem de automóveis da Volkswagen Autoeuropa. O estudo delineado foi dividido em dois cenários: validação e avaliação. A validação consistiu em comparar os dados provisionados por um sistema inercial de referência e determinados através dos métodos desenvolvidos. Para tal, usaram-se dados de sensores inerciais recolhidos em laboratório (N = 8 participantes) e nas linhas de montagem de automóveis (N = 9 participantes). A avaliação consistiu em quantificar a exposição biomecânica e consequente risco ergonómico respeitantes ao caso de estudo, empregando as estimativas angulares calculadas pela framework desenvolvida, e a partir dos dados recolhidos com o nosso sistema nas linhas de montagem de automóveis. Os resultados revelaram que a framework proposta tem o potencial para ser aplicada na monitorização de tarefas industriais. A avaliação ergonómica é mais lata através da medição direta, desvendando diferenças de exposição biomecânica e consequente risco ergonómico entre operadores.Work-related musculoskeletal disorders (WRMSDs) represent 15% of the total number of life-years lost due to work-related injuries and illness. Among WRMSDs’ risk factors, work-related postures are underlined in this research. Biomechanical exposure to hazardous postures negatively impacts workers’ health, enterprises’ economy, and society. Toward the apperception about the recurrent practice of hazardous postures in the workplace, self-reported, observational, and directly measured ergonomic assessment methods have been established. However, only self-reported and observational approaches are enforced on a more frequent basis, besides directly measured is a more compelling choice. The advent of the Internet of Things poses the opportunity of using wearables in the direction of ubiquitous data collection, increasing the amount of available data for a more personal and non-biased ergonomic evaluation. As follows, over workplace ergonomics research, wearables have been used to monitor human motion. The dissertation developed an automatic approach to ergonomic evaluation in industrial contexts. Its main contributions are the development of (1) a motion capture routine using inertial sensors; (2) a computational framework to monitor human upper body motion, in terms of joints’ angles, through inverse kinematics; and (3) computational implementations of posture risk factors specifications to quantify the biomechanical exposure and consequent ergonomic risk in occupational settings. Subsequently, specifications implementations were applied to provide insights in consideration of a case study from Volkswagen Autoeuropa automotive assembly lines. The research was divided into two scenarios: validation and evaluation. Validation consisted of comparing data provided by a ground truth inertial motion capture system and computed throughout the developed methods. Hence, inertial sensors’ data, collected in the laboratory (N = 8 participants) and automotive assembly lines (N = 9 participants) settings, were used. The evaluation consisted of quantifying the biomechanical exposure and consequent ergonomic risk concerning the case study, using angular estimates computed through the developed framework and about data collected in automotive assembly lines. The results revealed that the proposed framework has the potential to be applied to monitor industrial tasks. The ergonomic evaluation is more comprehensive through direct measures, uncovering differences about biomechanical exposure and consequent ergonomic risk among operators

Constructing a reference standard for sports science and clinical movement sets using IMU-based motion capture technology

Motion analysis has improved greatly over the years through the development of low-cost inertia sensors. Such sensors have shown promising accuracy for both sport and medical applications, facilitating the possibility of a new reference standard to be constructed. Current gold standards within motion capture, such as high-speed camera-based systems and image processing, are not suitable for many movement-sets within both sports science and clinical movement analysis due to restrictions introduced by the movement sets. These restrictions include cost, portability, local environment constraints (such as light level) and poor line of sight accessibility. This thesis focusses on developing a magnetometer-less IMU-based motion capturing system to detect and classify two challenging movement sets: Basic stances during a Shaolin Kung Fu dynamic form, and severity levels from the modified UPDRS (Unified Parkinson’s Disease Rating Scale) analysis tapping exercise. This project has contributed three datasets. The Shaolin Kung Fu dataset is comprised of 5 dynamic movements repeated over 350 times by 8 experienced practitioners. The dataset was labelled by a professional Shaolin Kung Fu master. Two modified UPDRS datasets were constructed, one for each of the two locations measured. The modified UPDRS datasets comprised of 5 severity levels each with 100 self-emulated movement samples. The modified UPDRS dataset was labelled by a researcher in neuropsychological assessment. The errors associated with IMU systems has been reduced significantly through a combination of a Complementary filter and applying the constraints imposed by the range of movements available in human joints. Novel features have been extracted from each dataset. A piecewise feature set based on a moving window approach has been applied to the Shaolin Kung Fu dataset. While a combination of standard statistical features and a Durbin Watson analysis has been extracted from the modified UPDRS measurements. The project has also contributed a comparison of 24 models has been done on all 3 datasets and the optimal model for each dataset has been determined. The resulting models were commensurate with current gold standards. The Shaolin Kung Fu dataset was classified with the computational costly fine decision tree algorithm using 400 splits, resulting in: an accuracy of 98.9%, a precision of 96.9%, a recall value of 99.1%, and a F1-score of 98.0%. A novel approach of using sequential forward feature analysis was used to determine the minimum number of IMU devices required as well as the optimal number of IMU devices. The modified UPDRS datasets were then classified using a support vector machine algorithm requiring various kernels to achieve their highest accuracies. The measurements were repeated with a sensor located on the wrist and finger, with the wrist requiring a linear kernel and the finger a quadratic kernel. Both locations achieved an accuracy, precision, recall, and F1-score of 99.2%. Additionally, the project contributed an evaluation to the effect sensor location has on the proposed models. It was concluded that the IMU-based system has the potential to construct a reference standard both in sports science and clinical movement analysis. Data protection security and communication speeds were limitations in the system constructed due to the measured data being transferred from the devices via Bluetooth Low Energy communication. These limitations were considered and evaluated in the future works of this project