Is Dual task testing more sensitive to detect gait improvement after a balance training program?

There is a growing interest in the role played by attention and cognitive processing in the control of posture and gait, investigated by means of dual task methodology. The rationale for dual task testing is to assess whether the simultaneous performance of two (or more) attention- demanding tasks exceed the central information-processing capacity. Recently, we proposed a new ambulatory system for gait analysis (Physilog®) and validated its performances in studying normal and abnormal walking [1]. This system measures temporal and spatial parameters of gait during long period of walking and therefore provides stride-to- stride variability of these parameters. In this pilot study, we used the Physilog® system to assess, under both single and dual task conditions, gait changes in 6 elderly subjects after a ten- weeks balance training program. Our objective was to determine whether dual task testing would be more sensitive than single task testing in detecting gait improvement in these elderly persons

Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation

Kamiar Aminian, Anisoara Ionescu, Bijan Najafi

The physical activity in normal daily life is determined to a large extent by the functional ability of a subject. As a result, the measurement of the physical activity that a subject performs spontaneously could be a useful and objective measurement of disability, particularly in patients with disease-related functional impairment. The aim of this study is to provide an accurate method for the measurement and analysis of the physical activity under normal life conditions. Using three kinematical sensors strapped to the body, both the posture and the gait parameters can be assessed qualitatively and quantitatively. A detailed description of the algorithms used to analyse both the posture and the gait are presented in this paper. Two methods, based on different sensor configurations and signal processing, are proposed for the detection of sitting and standing postures (Methods P1 and P2). Two other methods are used for the quantitative assessment of walking (Methods W1 and W2). The performance of the algorithms (expressed in terms of sensitivity, specificity and error) is based on the comparison of data recorded simultaneously by a non-interfering observer (reference data) with the data provided by the recording system (21 patients, 61 h). Sensitivity and specificity are respectively 98.2% and 98.8% (P1), 97.8% and 98.1% (P2) for sitting; 98.0% and 98.5% (P1), 97.4% and 97.8% (P2) for standing; 97.1% and 97.9% (W1), 92.4% and 94.9% (W2) for walking; and finally, 99.2% and 98.6% for lying. Overall detection errors (as a percent of range) are as follows: 1.15% (P1) and 1.20% (P2) for sitting, 1.36% (P1) and 1.40% (P2) for standing, 1.20% (W1) and 1.60% (W2) for walking and 0.40% for lying. The error for the estimated walking distance and the speed is 6.8% and 9.6%, respectively. We conclude that both methods can be used for the accurate measurement of the basic physical activity in normal daily life. Measurements performed before and after the delivery of a treatment can therefore provide information of unprecedented accuracy and objectivity on the ability of a procedure, in this case spinal cord stimulation, to restore functional capabilities. (C) 2003 Elsevier B.V. All rights reserved.

2004

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

Gait and Foot Clearance Parameters Obtained Using Shoe-Worn Inertial Sensors in a Large-Population Sample of Older Adults

Kamiar Aminian, Farzin Dadashi, Benoît Mariani

In order to distinguish dysfunctional gait; clinicians require a measure of reference gait parameters for each population. This study provided normative values for widely used parameters in more than 1400 able-bodied adults over the age of 65. We also measured the foot clearance parameters (i.e., height of the foot above ground during swing phase) that are crucial to understand the complex relationship between gait and falls as well as obstacle negotiation strategies. We used a shoe-worn inertial sensor on each foot and previously validated algorithms to extract the gait parameters during 20 m walking trials in a corridor at a self-selected pace. We investigated the difference of the gait parameters between male and female participants by considering the effect of age and height factors. Besides; we examined the inter-relation of the clearance parameters with the gait speed. The sample size and breadth of gait parameters provided in this study offer a unique reference resource for the researchers.