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.

Non-MTC gait cycles: An adaptive toe trajectory control strategy in older adults.

Minimum-toe-clearance (MTC) above the walking surface is a critical representation of toe-trajectory control due to its association with tripping risk. Not all gait cycles exhibit a clearly defined MTC within the swing phase but there have been few previous accounts of the biomechanical characteristics of non-MTC gait cycles. The present report investigated the within-subject non-MTC gait cycle characteristics of 15 older adults (mean 73.1 years) and 15 young controls (mean 26.1 years). Participants performed the following tasks on a motorized treadmill: preferred speed walking, dual task walking (carrying a glass of water) and a dual-task speed-matched control. Toe position-time coordinates were acquired using a 3 dimensional motion capture system. When MTC was present, toe height at MTC (MTCheight) was extracted. The proportion of non-MTC gait cycles was computed for the age groups and individuals. For non-MTC gait cycles an 'indicative' toe height at the individual's average swing phase time (MTCtime) for observed MTC cycles was averaged across multiple non-MTC gait cycles. In preferred-speed walking Young demonstrated 2.9% non-MTC gait cycles and Older 18.7%. In constrained walking conditions both groups increased non-MTC gait cycles and some older adults revealed over 90%, confirming non-MTC gait cycles as an ageing-related phenomenon in lower limb trajectory control. For all participants median indicative toe-height on non-MTC gait cycles was greater than median MTCheight. This result suggests that eliminating the biomechanically hazardous MTC event by adopting more of the higher-clearance non-MTC gait cycles, is adaptive in reducing the likelihood of toe-ground contact.

A machine learning approach to estimate Minimum Toe Clearance using Inertial Measurement Units.

Falls are the primary cause of accidental injuries (52%) and one of the leading causes of death in individuals aged 65 and above. More than 50% of falls in healthy older adults are due to tripping while walking. Minimum toe clearance (i.e., minimum height of the toe above the ground during the mid-swing phase - MTC) has been investigated as an indicator of tripping risk. There is increasing demand for practicable gait monitoring using wearable sensors such as Inertial Measurement Units (IMU) comprising accelerometers and gyroscopes due to their wearability, compactness and low cost. A major limitation however, is intrinsic noise making acceleration integration unreliable and inaccurate for estimating MTC height from IMU data. A machine learning approach to MTC height estimation was investigated in this paper incorporating features from both raw and integrated inertial signals to train Generalized Regression Neural Networks (GRNN) models using a hill-climbing feature-selection method. The GRNN based MTC height predictions demonstrated root-mean-square-error (RMSE) of 6.6mm with 9 optimum features for young adults and 7.1mm RMSE with 5 features for the older adults during treadmill walking. The GRNN based MTC height estimation method devised in this project represents approximately 68% less RMSE than other estimation techniques. The research findings show a strong potential for gait monitoring outside the laboratory to provide real-time MTC height information during everyday locomotion.

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.

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 (m x 1) 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 m x 1 was 5.32 mm and for MTC was 4.04 mm. Further application of a hill-climbing 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.