System and method for 3d gait assessment

The invention relates to a system and a method for assessment of walking and running gait in human. The method is preferably based on the fusion of a portable device featuring inertial sensors and several new dedicated signal processing algorithms: the detection of specific temporal events and parameters, 5 optimized fusion and de-drifted integration of inertial signals, automatic and online virtual alignment of sensors module, 3D foot kinematics estimation, a kinematic model for automatic online heel and toe position estimation, and finally the extraction of relevant and clinically meaning-full outcome parameters. Advantageously including at least one wireless inertial module attached to foot, the system provides common spatio-temporal parameters (gait cycle time, stride length, and stride velocity), with the 10 advantage of being able to work in unconstrained condition such as during turning or running. It furthermore may provide original parameters for each gait cycle, both temporal (load, foot-flat and push duration) and spatial (foot clearance and turning angle), and their inter-cycles variability. The system and method according to the invention allows the assessment of various aspects of gait which have shown recently to be of premium importance in research and clinical field, including foot clearance, 15 turns, gait initiation and termination, running, or gait variability. The system may be light weight, easy to wear and use, and suitable for any application requiring objective and quantitative evaluation of gait without heavy laboratory settings.

Heel and Toe Clearance Estimation for Gait Analysis Using Wireless Inertial Sensors

Kamiar Aminian, Benoît Mariani

Tripping is considered a major cause of fall in older people. Therefore, foot clearance (i.e., height of the foot above ground during swing phase) could be a key factor to better understand the complex relationship between gait and falls. This paper presents a new method to estimate clearance using a foot-worn and wireless inertial sensor system. The method relies on the computation of foot orientation and trajectory from sensors signal data fusion, combined with the temporal detection of toe-off and heel-strike events. Based on a kinematic model that automatically estimates sensor position relative to the foot, heel and toe trajectories are estimated. 2-D and 3-D models are presented with different solving approaches, and validated against an optical motion capture system on 12 healthy adults performing short walking trials at self-selected, slow, and fast speed. Parameters corresponding to local minimum and maximum of heel and toe clearance were extracted and showed accuracy +/- precision of 4.1 +/- 2.3 cm for maximal heel clearance and 1.3 +/- 0.9 cm for minimal toe clearance compared to the reference. The system is lightweight, wireless, easy to wear and to use, and provide a new and useful tool for routine clinical assessment of gait outside a dedicated laboratory.

Institute of Electrical and Electronics Engineers2012

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

Benoît Mariani

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.

EPFL2012

In-field objective evaluation and analysis in ski jumping using inertial sensors

Julien Chardonnens

Ski jumping, a prestigious Olympic discipline, has been constantly evolving. Over the years, the biomechanics fundamentals of this highly technical sport have been intensively documented by investigating the jump timing, kinematics, dynamics and motor control. This has increased our understanding of this discipline and resulted in the enhancement of the performance, in the development of new jumping techniques and in implications in safety and fairness. However, the performed analyses, based on standard measurement devices (e.g., 3D camera-based system, force plate), suffered from their complexity, time-consumption and related expenses. Consequently, they have been restricted to few athletes and focused on specific portions of the jump. On the other hand, to be compliant with the coaches’ and athletes’ needs, performance evaluation in daily routine relies primarily on feeling, visual observation and standard video analysis, but this type of evaluation suffers from subjectivity. Thus, nowadays, both scientists and field experts agree that there is a lack of an objective and simple assessment method for ski jumping. In this thesis, we propose and assess a new wearable inertial-based system and dedicated methods usable in training practice to extract timing, angular, dynamic, and coordination parameters during the entire jump. Then, we use the system to characterize the entire ski jump and evaluate the key parameters relative to performance using a relatively large cohort of athletes. The proposed measurement tool was devised to be easy-to-handle by coaches or athletes without the need for technical assistance. Within five minutes, the system was set up on the athlete and didn’t require any calibration constraints. The jumper was then ready for training as usual. After the training session, data were downloaded and then processed automatically in few seconds with the use of appropriate software. The key temporal events and related ski jumping phases were automatically detected on the raw inertial signals using a pattern recognition technique. A new algorithm was designed to monitor robust and drift-free orientations of skis and body segments for the entire jump sequence using a fusion of the inertial signals and specific biomechanical constraints during the jump. The dynamics of the take-off was evaluated using a forward kinematics model, while the flight aerodynamics was directly assessed using the inertial signals. The continuous relative phase was proposed to characterize the inter-segment coordination of lower-limbs during take-off. To assess the jump performance, traditional as well as new parameters describing the timing, the kinematics, dynamics and coordination were extracted. The proposed methods were assessed by their validity against reference measures when possible, by their consistency with the literature, and by their sensitivity to discriminate performance changes using athletes of different levels. To characterize the entire ski jumping sequence, 33 athletes, ranging from junior to world-class, were monitored during training conditions (in total, 87 jumps recorded). The temporal, angular, coordination, and dynamic classes of parameters were extracted using the wearable inertial system for the take-off, early-flight, and stable flight, and they were compared to performance (the jump length) using different statistical approaches, i.e. bivariate correlation, multiple regression (partial least-square regression), and factor analysis. It showed that ski jumping is a complex sequence of movements involving numerous parameters. This thesis proposed and assessed original inertial-based methods to evaluate the timing, kinematics, dynamics, and coordination in ski jumping. The measurement tool automatically extracts valid and sensitive parameters to characterize the performance and provides new insights by analysing the movement over the entire jump sequence. Giving a quantitative, rapid and personalized feedback with this new simple wearable system will help the athletes and coaches to better assess specific skills and improve the training efficiency.

EPFL2012

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

Benoît Mariani

EPFL2012

In-field objective evaluation and analysis in ski jumping using inertial sensors

Julien Chardonnens

EPFL2012