Minimum toe clearance events in divided attention treadmill walking in older and young adults: A cross-sectional study

BACKGROUND: Falls in older adults during walking frequently occur while performing a concurrent task; that is, dividing attention to respond to other demands in the environment. A particularly hazardous fall-related event is tripping due to toe-ground contact during the swing phase of the gait cycle. The aim of this experiment was to determine the effects of divided attention on tripping risk by investigating the gait cycle event Minimum Toe Clearance (MTC). METHODS: Fifteen older adults (mean 73.1 years) and 15 young controls (mean 26.1 years) performed three walking tasks on motorized treadmill: (i) at preferred walking speed (preferred walking), (ii) while carrying a glass of water at a comfortable walking speed (dual task walking), and (iii) speed-matched control walking without the glass of water (control walking). Position-time coordinates of the toe were acquired using a 3 dimensional motion capture system (Optotrak NDI, Canada). When MTC was present, toe height at MTC (MTC_Height) and MTC timing (MTC_Time) were calculated. The proportion of non-MTC gait cycles was computed and for non-MTC gait cycles, toe-height was extracted at the mean MTC_Time. RESULTS: Both groups maintained mean MTC_Height across all three conditions. Despite greater MTC_Height SD in preferred gait, the older group reduced their variability to match the young group in dual task walking. Compared to preferred speed walking, both groups attained MTC earlier in dual task and control conditions. The older group’s MTC_Time SD was greater across all conditions; in dual task walking, however, they approximated the young group’s SD. Non-MTC gait cycles were more frequent in the older group across walking conditions (for example, in preferred walking: young – 2.9 %; older - 18.7 %). CONCLUSIONS: In response to increased attention demands older adults preserve MTC_Height but exercise greater control of the critical MTC event by reducing variability in both MTC_Height and MTC_Time. A further adaptive locomotor control strategy to reduce the likelihood of toe-ground contacts is to attain higher mid-swing clearance by eliminating the MTC event, i.e. demonstrating non-MTC gaits cycles

Support vector machines for young and older gait classification using inertial sensor kinematics at minimum toe clearance

Paper originally presented at: 10th EAI International Conference on Body Area Networks (BODYNETS 2015) Sydney, Australia, 28-30 Sept, 201

Machine-Learning Applications to Gait Biomechanics using Inertial Sensor Signals

Minimum toe clearance (MTC) above the walking surface is a critical representation of toe-trajectory control related to tripping risk. Reliable and precise MTC measurements are obtained in the laboratory using 3D motion capture technology. Real-world gait monitoring using body-mounted sensors presents considerable data processing challenges when estimating kinematic parameters, including MTC. This Thesis represents the first study employing machine-learning to estimate young and older adults’ toe-height at MTC using inertial data captured from a foot-mounted sensor. Age-group specific Generalized Regression Neural Network (GRNN) models estimated MTC with root-mean-square-error (RMSE) of 6.6 mm with 9 optimum inertial-signal features for the young and 7.1 mm with 5 features for the older during treadmill walking. These RMSE values are approximately one third of the previously reported (Mariani et al., 2012; McGrath et al., 2011) and GRNN modeling also performed well as reflected in no significant difference between 3D measured reference and model estimated MTC_Height. The GRNN model specific to older adults showed good generalizability when applied to data from slower and dual task walking

Correlations between end point foot trajectories and inertial sensor data

Inertial measurement units (IMU) comprising accelerometers and gyroscopes have recently found use in a wide range of motion analysis applications. Even though the technique of inferring secondary parameters such as velocity, displacement and angular rotation is greatly influenced by sensor noise, IMUs continue to find interest in movement studies due to other attractive properties such as light weight, wearability, compactness and low cost. In this study, the motivation was to determine the relationship between the raw foot inertial sensor data and the key end point foot trajectories. Correlations were calculated between the sensor data and the end points obtained from an optoelectronic motion analysis system, i.e. Optotrak Certus, NDI. It was found that vertical acceleration of the toe 0.06 s prior to toe-off were the most correlated with the end point foot trajectories. During toe-off, the amount of rotation of the distal foot was also significantly correlated with the first maximum - m×1 of the foot clearance. Foot rotation direction however varied between subjects tested suggesting different gait initiation strategies. The results are encouraging because they indicate that raw inertial sensor measurements are correlated with vertical foot end point trajectories, and could be used to detect the risk of falling

Estimation of end point foot clearance points from inertial sensor data

Foot clearance parameters provide useful insight into tripping risks during walking. This paper proposes a technique for the estimate of key foot clearance parameters using inertial sensor (accelerometers and gyroscopes) data. Fifteen features were extracted from raw inertial sensor measurements, and a regression model was used to estimate two key foot clearance parameters: First maximum vertical clearance (mx1) after toe-off and the Minimum Toe Clearance (MTC) of the swing foot. Comparisons are made against measurements obtained using an optoelectronic motion capture system (Optotrak), at 4 different walking speeds. General Regression Neural Networks (GRNN) were used to estimate the desired parameters from the sensor features. Eight subjects foot clearance data were examined and a Leave-one-subject-out (LOSO) method was used to select the best model. The best average Root Mean Square Errors (RMSE) across all subjects obtained using all sensor features at the maximum speed for mx1 was 5.32 mm and for MTC was 4.04 mm. Further application of a hillclimbing feature selection technique resulted in 0.54-21.93% improvement in RMSE and required fewer input features. The results demonstrated that using raw inertial sensor data with regression models and feature selection could accurately estimate key foot clearance parameters

A Note on Octonionic Support Vector Regression

This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited. It is shown that, in the general case of an octonionic-valued feature map, the usual “kernel trick” breaks down. The cause of this (and its interpretation) is discussed in some detail, along with potential ways of extending kernel methods to take advantage of the distinct features present in the general case. Finally, the octonionic SVR is applied to an example gait analysis problem, and its performance is compared to that of the least squares SVR, the Clifford SVR, and the multidimensional SVR