Robust foot clearance estimation based on the integration of foot-mounted IMU acceleration data

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject's foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions

Automated gait segmentation and tracking using inertial measurement units

Abstract. In this thesis, a methodology is presented to automate the labelling, event detection, segmentation, tracking, and parameter extraction of IMU gait data for sensors placed on the feet and shanks. The algorithms presented were tested using IMU data from three different styles of gait, normal gait, antalgic gait, and limited mobility gait. The algorithms developed were found effective for all of the simulated gait styles without mislabelling or detecting erroneous gait segments. The resultant gait trajectories and parameters were analyzed and were found to accurately depict the differences between each of the different styles of gait. The methodology presented can be used for the rapid and accurate processing of gait data for multiple styles of gait. This quantification of gait data can enable the collection of IMU gait data on a larger scale. This provides an accessible, low-cost option for out-of-laboratory gait data collection

Characterizing the Variability of Kinematic Outcome Measures and Compensatory Movements using Inertial Measurement Units

Cost-effective wearable sensors to measure movement have gained traction as research and clinical tools. The potential to quantify movement with a portable and inexpensive way could provide benefits to patient populations (e.g. amputees) to supplement or replace current clinical evaluations. For example, characterization of frontal plane kinematic outcome measures is a relevant movement pattern to a complex amputee population. The ability to capture such movements could have important therapeutic opportunities. The current research worked towards characterizing frontal plane compensatory movement patterns with kinematic outcome measures described by inertial measurement units (IMU) data in healthy adults. This was an initial step towards developing a future toolkit that could characterize normal and aberrant movement patterns in clinical populations. The thesis is comprised of two related studies. The first study set out to evaluate the numerical accuracy of IMU estimated spatial measures when compared to a gold standard system. Six subjects completed six different movement tasks while instrumented with optical motion capture and IMUs. Each movement task probed the accuracy of specific deviations (e.g. vertical deviation). The hypothesis was that outcome measures would be strongly associated (r>0.8) and mean error would not be significantly different from zero and the coefficient of repeatability would be within priori set limits of agreement (±18 mm). Kinematic outcome measures had small mean error bias compared to gold standard measures and range of subject specific mean errors showed minimal differences. Task specific differences were evident when movement patterns exhibit large transverse rotations. These results showed the devices have a level of accuracy that may be suitable to characterize changes in movement patterns clinically. The second study aimed to utilize the same techniques from study 1 to describe compensatory kinematic outcome measures during a clinical obstacle avoidance task to differentiate between compensatory and normal movement patterns. Twelve subjects wore IMUs bilaterally on the ankles and on the belt above the right hip. An off the shelf orthotic knee brace was used to restrict lower limb knee joint kinematics (reduce range of motion). Participants completed 15 walking trials for three different brace conditions (No Brace, Unlocked Brace, Locked Brace) and two obstacle task conditions (Level Ground Walking and Obstacle Avoidance) to elicit a comparison of normal and compensatory movements. During the walking task, IMUs were able to characterize compensatory movements typical of the amputee population. Lateral deviation of the swinging foot was significantly larger during obstacle crossing with a locked brace compared to no brace. Maximum elevation of the limb was significantly larger while crossing obstacles compared to level ground walking and was precise enough to discern elevation differences of No Brace elevation from both Unlocked and Locked Brace conditions. Hip hiking was also significantly larger in the locked brace obstacle crossing from no brace obstacle crossing. Swing time was longer when the limb was braced and during obstacle crossing when compared to level ground walking. Healthy subjects had no significant changes to double support time compared those exhibited by amputees during walking. Overall, differences between IMU and gold standard measures are present. Mean error differences are present for certain tasks and criteria for agreeability between devices is not satisfied. Descriptive analysis of low subject mean error ranges across the majority of tasks indicate a potential utility in these measures to distinguish between movement patterns. During the clinical task, when knee mobility was manipulated compensatory movements were significantly different across conditions. This study provides evidence for the utility of IMU devices to support clinical gait analysis with quantifiable measures

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions

A sensor fusion approach for inertial sensors based 3D kinematics and pathological gait assessments: toward an adaptive control of stimulation in post-stroke subjects

International audiencePathological gait assessment and assistive control based on functional electrical stimulation (FES) in post-stroke individuals, brings out a common need to robustly quantify kinematics facing multiple constraints. This study proposes a novel approach using inertial sensors to compute dorsiflexion angles and spatio-temporal parameters, in order to be later used as inputs for online close-loop control of FES. 26 post-stroke subjects were asked to walk on a pressure mat equipped with inertial measurement units (IMU) and passive reflective markers. A total of 930 strides were individually analyzed and results between IMU-based algorithms and reference systems compared. Mean absolute (MA) errors of dorsiflexion angles were found to be less than 4°, while stride lengths were robustly segmented and estimated with a MA error less than 10 cm. These results open new doors to rehabilitation using adaptive FES closed-loop control strategies in "foot drop" syndrome correction

Using deep learning to predict minimum foot–ground clearance event from toe-off kinematics

Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations

Recognition of gait patterns in human motor disorders using a machine learning approach

Dissertação de mestrado em Industrial Electronics and Computers EngineeringWith advanced age, the occurrence of motor disturbances becomes more prevalent and can lead to gait pathologies, increasing the risk of falls. Currently, there are many available gait monitoring systems that can aid in gait disorder diagnosis by extracting relevant data from a subject’s gait. This increases the amount of data to be processed in working time. To accelerate this process and provide an objective tool for a systematic clinical diagnosis support, Machine Learning methods are a powerful addition capable of processing great amounts of data and uncover non-linear relationships in data. The purpose of this dissertation is the development of a gait pattern recognition system based on a Machine Learning approach for the support of clinical diagnosis of post-stroke gait. This includes the development of a data estimation tool capable of computing several features from inertial sensors. Four different neural networks were be added to the classification tool: Feed-Forward (FFNN), convolutional (CNN) and two recurrent neural networks (LSTM and CLSTM). The performance of all classification models was analyzed and compared in order to select the most effective method of gait analysis. The performance metric used is Matthew’s Correlation Coefficient. The classifiers that exhibit the best performance where Support Vector Machines (SVM), k-Nearest Neighbors (KNN), CNN, LSTM and CLSTM, with a Matthew’s correlation coeficient of 1 in the test set. Despite the first two classifiers reaching the same performance of the three neural networks, the later reached this performance systematically and without the need of explicit dimensionality reduction methods.Com o avançar da idade, a ocorrência de distúrbios motores torna-se mais prevalente, conduzindo a patologias na marcha e aumentando o risco de quedas. Atualmente, muitos sistemas de monitorização de marcha extraem grandes quantidades de dados biomecânicos para apoio ao diagnóstico clínico, aumentando a quantidade de dados a ser processados em tempo útil. Para acelerar esse processo e proporcionar uma ferramenta objetiva de apoio sistemático ao diagnóstico clínico, métodos de Machine Learning são uma poderosa adição, processando grandes quantidades de dados e descobrindo relações não-lineares entre dados. Esta dissertação tem o objetivo de desenvolver um sistema de reconhecimento de padrões de marcha com uma abordagem de Machine Learning para apoio ao diagnóstico clínico da marcha de vitimas de AVC. Isso inclui o desenvolvimento de uma ferramenta de estimação de dados biomecânicos e cálculo de features, a partir de sensores inerciais. Quatro redes neuronais foram implementadas numa ferramenta de classificação: uma rede Feed-Forward (FFNN), uma convolucinal (CNN), e duas redes recorrentes (LSTM e CLSTM). O desempenho de todos os modelos de classificação foi analisado. A métrica de desempenho usada é o coeficiente de correlação de Matthew. Os classificadores com melhor performance foram: Support Vector Machines (SVM), k-Nearest Neighbors (KNN), CNN, LSTM e CLSTM. Todos com uma performance igual a 1 no conjunto de teste. Apesar de os dois primeiros classificadores atingirem a mesma performance das redes neuronais, estas atingiram esta performance repetidamente e sem necessitar de métodos de redução de dimensionalidade