Drift Reduction for Inertial Sensor Based Orientation and Position Estimation in the Presence of High Dynamic Variability During Competitive Skiing and Daily-Life Walking

Nowadays inertial sensors are extensively used for gait analysis. They can be used to perform temporal event detection (i.e. step detection) and to estimate the orientation of the feet and other body segments to determine walking speed and distance. Usually, orientation is estimated from integration of the measured angular velocity. Prior to integration of measured acceleration to obtain speed, the gravity component has to be estimated and removed. During each integration small measurement errors accumulate and result in so-called drift. Since the first uses of inertial sensors for gait analysis methods have been presented to model, estimate and remove the drift. The proposed methods worked well for relatively slow movements and movements taking place in the sagittal plane. Many methods also relied on periodically occurring static phases such as the stance phase during walking to correct the drift. Inertial sensors could also be used to track higher dynamic movements, for example in sports. Potential applications focus on two aspects: performance analysis and injury prevention. To better explain and predict performance, in-field measurements to assess the coordination, kinematics, and dynamics are key. While traditional movement analysis (e.g. video analysis) can answer most of the questions related to both performance and injury, they are cumbersome and complex to use in-field. Inertial sensors, however, are perfectly suited since they allow to measure the movement in any environment and are not restricted to certain capture volumes. Nevertheless, most sports have very high movement dynamics (e.g. fast direction changes, high speeds) and are therefore challenging for computing reliable estimates of orientation, speed and position. The inertial measurements are compromised by noise and movements oftentimes don't provide static or slow phases used in gait analysis for drift correction. Therefore, the present thesis aimed to propose and validate new methods to model, estimate and remove drift in sports and for movements taking place outdoors in uncontrolled environments. Three different strategies were proposed to measure the movement of classical cross-country skiing and ski mountaineering, alpine ski racing, and outdoor walking over several kilometres. For each activity specific biomechanical constraints and movement dynamics were exploited. The proposed methods rely only on inertial sensors and magnetometers and are able to provide orientation, speed, and position information with an accuracy and precision close to existing gold standards. The most complete system was designed in alpine ski racing, probably one of the most challenging sports for movement analysis. Extreme vibrations, high speeds of over 120 km/h and a timing resolution below 0.01 seconds require maximum accuracy and precision. The athlete's posture and the kinematics of his centre of mass both in a relative athlete-centred frame and in a global Earth-fixed frame could be obtained with high accuracy and precision. Where 3D video analysis requires a very complex experimental setup and takes several hours of post processing to analyse a single turn of a skier, the proposed system allows to measure multiple athletes and complete runs within minutes. Thus, new experimental designs to assess performance and injury risk in alpine ski racing became feasible, greatly helping to gain further knowledge about this highly complex and risky sport

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Algorithms for walking speed estimation using a lower-back-worn inertial sensor: A cross-validation on speed ranges

Walking/gait speed is a key measure for daily mobility characterization. To date, various studies have attempted to design algorithms to estimate walking speed using an inertial sensor worn on the lower back, which is considered as a proper location for activity monitoring in daily life. However, these algorithms were rarely compared and validated on the same datasets, including people with different preferred walking speed. This study implemented several original, improved, and new algorithms for estimating cadence, step length and eventually speed. We designed comprehensive cross-validation to compare the algorithms for walking slow, normal, fast, and using walking aids. We used two datasets, including reference data for algorithm validation from an instrumented mat (40 subjects) and shanks-worn inertial sensors (88 subjects), with normal and impaired walking patterns. The results showed up to 50% performance improvements. Training of algorithms on data from people with different preferred speeds led to better performance. For the slow walkers, an average RMSE of 2.5 steps/min, 0.04 m, and 0.10 m/s were respectively achieved for cadence, step length, and speed estimation. For normal walkers, the errors were 3.5 steps/min, 0.08 m, and 0.12 m/s. An average RMSE of 1.3 steps/min, 0.05 m, and 0.10 m/s were also observed on fast walkers. For people using walking aids, the error significantly increased up to an RMSE of 14 steps/min, 0.18 m, and 0.27 m/s. The results demonstrated the robustness of the proposed combined speed estimation approach for different speed ranges. It achieved an RMSE of 0.10, 0.18, 0.15, and 0.32 m/s for slow, normal, fast, and using walking aids, respectively

Steps parameters of elderly patients hospitalised for an acute medical illness in a Swiss University Hospital: a monocentric observational pilot-study.

oai:serval.unil.ch:BIB_3AA9F2A56CC4Objective mobility goals for elderly hospitalised medical patients remain debated. We therefore studied steps parameters of elderly patients hospitalised for an acute illness, to determine goals for future interventional trials and medical practice. Observational study conducted from February to November 2018 in a medical ward of the Lausanne University Hospital, Switzerland. We measured the step parameters of consecutive medical patients aged ≥65 years admitted for an acute medical illness using a wrist accelerometer (Geneactiv). We also collected demographic, somatic and functional factors. Overall, 187 inpatients had their step parameters (daily step count, walking cadence and bout duration) measured with accelerometers worn for a mean of 3.6 days (standard deviation [SD] 3.2). Elderly inpatients (81.5 years, SD 8.5) walked a median of 603 steps daily (interquartile range [IQR] 456-809), at a median cadence of 100 steps/minute (IQR 99-101) with median walking bouts of 33 seconds (IQR 27-37) and with 70% of the walking bouts lasting less than 30 seconds. Patients walking ≥600 steps were younger (80.4 years, SD 8.9 vs 82.8 years,SD 7.9, p = 0.050) and had a longer length of stay (7.8 days, SD 5.1 vs 6.1 days, SD 4.1, p = 0.011) than those walking <600 steps. Patients at high risk of bed sores walked less (564 steps, IQR 394-814 vs 626, IQR 526-840) than those with a lower risk of sores. During a hospitalisation for an acute medical illness, patients aged ≥65 years walk a mere 603 steps daily and most of the time for periods of less than 30 seconds. This information should be used to build up future interventional trials or to set mobility goals for patients hospitalised in Swiss hospitals

Validity of Step Counting Methods over One Day in a Free-Living Environment

This thesis was designed in two parts to determine the step count accuracy of activity monitors in a free-living environment. The aims of the first and second part of the study were to (1) critically evaluate the effects on step counts using the study methodology of wearing multiple monitors on the same area of the body and to (2) determine the step count accuracy of numerous consumer- and research-grade activity monitors worn on various locations of the body across all hours of a day in a free-living environment, respectively. For both parts of the study, the same hip- and wrist-worn monitor brands were examined. Wrist monitors included the ActiGraph GT9X (GT9X), Fitbit Alta (FA), Garmin Vivofit 3 (GV), and Apple Watch Series 2 (ApW). Hip monitors included the ActiGraph GT9X (GT9X), Fitbit Zip (FZ), Omron HJ-325 (OM), Yamax Digiwalker SW-200 (YX). In the second part of the study, a thigh-worn monitor, activPAL (AP), was also examined

Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns

In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30-40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50)

A wearable biofeedback device to improve motor symptoms in Parkinson’s disease

Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the University of Minho. For validation purposes and data acquisition, it was developed a collaboration with the Clinical Academic Center (2CA), located at Braga Hospital. The knowledge acquired in the development of this master thesis is linked to the motor rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also to be used as a clinical decision support tool for the classification of the motor disability level. This system provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease. The system is based on a user- centered design, considering the end-user driven, multitasking and less cognitive effort concepts. This manuscript presents all steps taken along this dissertation regarding: the literature review and respective critical analysis; implemented tech-based procedures; validation outcomes complemented with results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração com Clinical Academic Center (2CA), localizado no Hospital de Braga. Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto, esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS) utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador, considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo. Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica implementados; resultados de validação complementados com discussão de resultados; e principais conclusões e desafios futuros

Objective assessment of movement disabilities using wearable sensors

The research presents a series of comprehensive analyses based on inertial measurements obtained from wearable sensors to quantitatively describe and assess human kinematic performance in certain tasks that are most related to daily life activities. This is not only a direct application of human movement analysis but also very pivotal in assessing the progression of patients undergoing rehabilitation services. Moreover, the detailed analysis will provide clinicians with greater insights to capture movement disorders and unique ataxic features regarding axial abnormalities which are not directly observed by the clinicians

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation