Walking Speed of Children and Adolescents With Cerebral Palsy: Laboratory Versus Daily Life

The purpose of this pilot study was to compare walking speed, an important component of gait, in the laboratory and daily life, in young individuals with cerebral palsy (CP) and with typical development (TD), and to quantify to what extent gait observed in clinical settings compares to gait in real life. Fifteen children, adolescents and young adults with CP (6 GMFCS I, 2 GMFCS II, and 7 GMFCS III) and 14 with TD were included. They wore 4 synchronized inertial sensors on their shanks and thighs while walking at their spontaneous self-selected speed in the laboratory, and then during 2 week-days and 1 weekend day in their daily environment. Walking speed was computed from shank angular velocity signals using a validated algorithm. The median of the speed distributions in the laboratory and daily life were compared at the group and individual levels using Wilcoxon tests and Spearman's correlation coefficients. The corresponding percentile of daily life speed equivalent to the speed in the laboratory was computed and observed at the group level. Daily-life walking speed was significantly lower compared to the laboratory for the CP group (0.91 [0.58-1.23] m/s vs 1.07 [0.73-1.28] m/s,p= 0.015), but not for TD (1.29 [1.24-1.40] m/s vs 1.29 [1.20-1.40] m/s,p= 0.715). Median speeds correlated highly in CP (p< 0.001, rho = 0.89), but not in TD. In children with CP, 60% of the daily life walking activity was at a slower speed than in-laboratory (corresponding percentile = 60). On the contrary, almost 60% of the daily life activity of TD was at a faster speed than in-laboratory (corresponding percentile = 42.5). Nevertheless, highly heterogeneous behaviors were observed within both populations and within subgroups of GMFCS level. At the group level, children with CP tend to under-perform during natural walking as compared to walking in a clinical environment. The heterogeneous behaviors at the individual level indicate that real-life gait performance cannot be directly inferred from in-laboratory capacity. This emphasizes the importance of completing clinical gait analysis with data from daily life, to better understand the overall function of children with CP.

A Personalized Approach to Improve Walking Detection in Real-Life Settings: Application to Children with Cerebral Palsy

Kamiar Aminian, Stéphane Armand, Lena Carcreff, Anisoara Ionescu

Although many methods have been developed to detect walking by using body-worn inertial sensors, their performances decline when gait patterns become abnormal, as seen in children with cerebral palsy (CP). The aim of this study was to evaluate if fine-tuning an existing walking bouts (WB) detection algorithm by various thresholds, customized at the individual or group level, could improve WB detection in children with CP and typical development (TD). Twenty children (10 CP, 10 TD) wore 4 inertial sensors on their lower limbs during laboratory and out-laboratory assessments. Features extracted from the gyroscope signals recorded in the laboratory were used to tune thresholds of an existing walking detection algorithm for each participant (individual-based personalization: Indiv) or for each group (population-based customization: Pop). Out-of-laboratory recordings were analyzed for WB detection with three versions of the algorithm (i.e., original fixed thresholds and adapted thresholds based on the Indiv and Pop methods), and the results were compared against video reference data. The clinical impact was assessed by quantifying the effect of WB detection error on the estimated walking speed distribution. The two customized Indiv and Pop methods both improved WB detection (higher, sensitivity, accuracy and precision), with the individual-based personalization showing the best results. Comparison of walking speed distribution obtained with the best of the two methods showed a significant difference for 8 out of 20 participants. The personalized Indiv method excluded non-walking activities that were initially wrongly interpreted as extremely slow walking with the initial method using fixed thresholds. Customized methods, particularly individual-based personalization, appear more efficient to detect WB in daily-life settings.

2019

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

The effects of dual tasks on gait in children with cerebral palsy

Kamiar Aminian, Stéphane Armand, Lena Carcreff

Aim: To assess the gait and cognitive performances of children with cerebral palsy (CP) during dual tasks (DT) in comparison to typically developing (TD) children. Method: This prospective, observational, case-control study included 18 children with CP (7 girls, 11 boys; median age 12 [10:13] years and 19 controls (9 girls, 10 boys; median age 12 [10:13y6mo] years). Performances were recorded during a simple walking task, 5 DT (walking + cognitive tasks with increasing cognitive load), and 5 simple cognitive tasks (while sitting). Gait parameters were computed using an optoelectronic system during walking tasks. Six parameters were selected for analysis by a principal component analysis. Cognitive performance was measured for each cognitive task. The dual-task cost (DTC) was calculated for each DT. Results: Gait performance decreased in both groups as DT cognitive load increased (e.g., walking speed normalized by leg length, in simple task: 1.25 [1.15:1.46] s −1 for CP, 1.53 [1.38:1.62] s −1 for TD; DT with highest load: 0.64 [0.53:0.80] s −1 for CP, 0.95 [0.75:1.08] s −1 for TD). The CP group performed significantly worse than TD group in every task (including the simple task), but DTC were similar in both groups. A task effect was found for the majority of the gait parameters. Interpretation: The reduced gait performance induced by DT may generate underestimated difficulties for children with CP in daily-life situations, where DT are common. This should be considered in clinical assessments.

2019

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

Benoît Mariani

EPFL2012

A Personalized Approach to Improve Walking Detection in Real-Life Settings: Application to Children with Cerebral Palsy

Kamiar Aminian, Stéphane Armand, Lena Carcreff, Anisoara Ionescu

2019