Automatic identification of gait events using an instrumented sock

Background: textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait. Methods: we investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal. Results: our results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability. Conclusions: this study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance

A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors

A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots

AUTOMATED MULTI-FEATURE SEGMENTATION OF TREADMILL RUNNING

The definition of gait events and phases have been well established in the literature through the use of qualitative movement descriptors. The repeatable, objective definitions of gait events and phases is the cornersone of sucess when performin a multi-center trial. A correlation-based multi-feature automated segmentation algorithm was developed and applied to treadmill running data. The features used were soley from 3D kinematic marker trajectory data, including generated features such as vectors between kinematic markers. The algorithm was compared against a trained tester who used visual inspection and threshold limits of the vGRF to segment stance. The automated segmentation approach was shown to consistently identify the same gait events as the trained tester, representing a significant time savings for the signal processing of large volume treadmill running data

Sensors for triggering practical Functional Electrical Stimulation walking systems

Functional Electrical Stimulation (FES) techniques have shown significant improvement in mobility and functionality to many patients with pathological gait resulting from upper motor neurological injuries such as stroke, Multiple Sclerosis (MS), etc. Effective functioning of FES walking systems relies on accurate and reliable detection of gait events (i.e heel rise and heel strike) which depends on the type of sensors and the detection algorithm used

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

Foot kinematics in patients with two patterns of pathological plantar hyperkeratosis

Background: The Root paradigm of foot function continues to underpin the majority of clinical foot biomechanics practice and foot orthotic therapy. There are great number of assumptions in this popular paradigm, most of which have not been thoroughly tested. One component supposes that patterns of plantar pressure and associated hyperkeratosis lesions should be associated with distinct rearfoot, mid foot, first metatarsal and hallux kinematic patterns. Our aim was to investigate the extent to which this was true. Methods: Twenty-seven subjects with planter pathological hyperkeratosis were recruited into one of two groups. Group 1 displayed pathological plantar hyperkeratosis only under metatarsal heads 2, 3 and 4 (n = 14). Group 2 displayed pathological plantar hyperkeratosis only under the 1st and 5th metatarsal heads (n = 13). Foot kinematics were measured using reflective markers on the leg, heel, midfoot, first metatarsal and hallux. Results: The kinematic data failed to identify distinct differences between these two groups of subjects, however there were several subtle (generally <3°) differences in kinematic data between these groups. Group 1 displayed a less everted heel, a less abducted heel and a more plantarflexed heel compared to group 2, which is contrary to the Root paradigm. Conclusions: There was some evidence of small differences between planter pathological hyperkeratosis groups. Nevertheless, there was too much similarity between the kinematic data displayed in each group to classify them as distinct foot types as the current clinical paradigm proposes

Timetable of Gait Cycle Events in Parkinson's Disease.

The study used an algorithmic method to measure fluctuations in the timetable of gait cycle events in patients with Parkinson's disease (PD). Subjects with severe PD (n=10; age 63.6 ± 10.1 years; Hoehn & Yahr [H & Y] disability score 3 or 4), mild PD (n=10; age 65.5 ± 4.3; H & Y ≦ 2), and normal controls (n=10; age 65.1 ± 13.3) were studied. A camera was mounted on the trunk, and the subjects walked in a self-selected manner. Overhead images of the foot path were analyzed to geometrically describe motion in terms of displacement and velocity. The timing of three gait events, i.e.,¹⁾ feet adjacent,²⁾ maximum speed of swinging foot, and³⁾ the trunk climbing to its highest point in mid-stance, was determined for extracted steps during steady-state gait. In severe PD, 74.9 ± 21.7% of steps was timetabled so that the swinging leg and the stance-phase leg became side by side before the trunk rose to its highest point to achieve 'foot clearance'. This pattern was significantly less prevalent in mild PD and controls. An altered timetable of gait cycle events may provide quantitative indices of gait disability during steady-state walking in patients with PD

3-D kinematic comparison of treadmill and overground running.

Studies investigating the mechanics of human movement are often conducted using the treadmill. The treadmill is an attractive device for the analysis of human locomotion. Studies comparing overground and treadmill running have analyzed discrete variables, however differences in excursion from footstrike to peak angle and range of motion during stance have yet to be examined. This study aimed to examine the 3-D kinematics of the lower extremities during overground and treadmill locomotion to determine the extent to which the two modalities differ. Twelve participants ran at 4.0m/s in both treadmill and overground conditions. 3-D angular kinematic parameters during the stance phase were collected using an eight camera motion analysis system. Hip, knee and ankle joint kinematics were quantified in the sagittal, coronal and transverse planes, then compared using paired t-tests. Of the parameters analyzed hip flexion at footstrike 12° hip range of motion 17°, peak hip flexion 12.7°, hip transverse plane range of motion 8° peak knee flexion 5° and peak ankle excursion range 6.6°, coronal plane ankle angle at toe-off 6.5° and peak ankle eversion 6.3° were found to be significantly different. These results lead to the conclusion that the mechanics of treadmill locomotion cannot be generalized to overground

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed