Energy flow analysis of the lower extremity during gait in persons with chronic stroke.

BACKGROUND: A decline in walking capacity and high energy cost can limit mobility following stroke. Mechanical energy exchange between lower limb and trunk segments can reflect gait inefficiencies, but reveals little about active energy flow between adjacent segments through muscle actions. This study evaluated mechanical energy expenditures (MEEs) during walking in stroke and healthy groups to understand movement control and explore the impact of walking speed on mechanical energy exchanges. METHODS: Thirteen adults with hemiparesis and six healthy controls walked at self-selected speed. Power curves for each lower limb joint were segmented into concentric and eccentric sources of muscle power and transfer/no-transfer modes to calculate MEEs during stance. FINDINGS: MEEs were lower in the stroke group on the affected side compared to the less affected side and compared to controls. Specifically, the affected plantarflexors transferred less energy distally via concentric action in late stance compared to the less affected side. However, the stroke group generated greater energy at the ankle in the absence of transfer compared to controls. Less concentrically transferred energy through midstance and absorbed in late stance was evident by the knee extensors bilaterally in stroke. At the hip, the total energy (no transfer) was reduced on the affected side. Classifying stroke subjects by walking speed (<.6m/s, >.6m/s) revealed disruptions in harnessing energy through motion and transfer energy across segments in the slower group. INTERPRETATION: The limited ability of those with stroke to exploit intersegmental energy transfer to optimize efficiency may limit endurance and functional independence.

Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors.

This paper represents the first step in developing an inertial sensor system that is capable of assessing post-stroke gait in terms of walking speed and temporal gait symmetry. Two inertial sensors were attached at the midpoint of each shank to measure the accelerations and angular velocity during walking. Despite the abnormalities in hemiparetic gait, the angular velocity of most of the testing subjects (12 out of 13) exhibited similar characteristics as those from a healthy population, enabling walking speed estimation and gait event detection based on the pendulum walking model. The results from a standardized 10-meter walk test demonstrated that the IMU-based method has an excellent agreement with the clinically used stopwatch method. The gait symmetry results were comparable with previous studies. The gait segmentation failed when the angular velocity deviates significantly from the healthy groups' profile. With further development and concurrent validations, the inertial sensor-based system may eventually become a useful tool for continually monitoring spatio-temporal gait parameters post stroke in a natural environment.

Inertial sensor-based methods in walking speed estimation: a systematic review.

Self-selected walking speed is an important measure of ambulation ability used in various clinical gait experiments. Inertial sensors, i.e., accelerometers and gyroscopes, have been gradually introduced to estimate walking speed. This research area has attracted a lot of attention for the past two decades, and the trend is continuing due to the improvement of performance and decrease in cost of the miniature inertial sensors. With the intention of understanding the state of the art of current development in this area, a systematic review on the exiting methods was done in the following electronic engines/databases: PubMed, ISI Web of Knowledge, SportDiscus and IEEE Xplore. Sixteen journal articles and papers in proceedings focusing on inertial sensor based walking speed estimation were fully reviewed. The existing methods were categorized by sensor specification, sensor attachment location, experimental design, and walking speed estimation algorithm.

Inertial sensors in estimating walking speed and inclination: an evaluation of sensor error models.

With the increasing interest of using inertial measurement units (IMU) in human biomechanics studies, methods dealing with inertial sensor measurement errors become more and more important. Pre-test calibration and in-test error compensation are commonly used to minimize the sensor errors and improve the accuracy of the walking speed estimation results. However, the performance of a given sensor error compensation method does not only depend on the accuracy of the calibration or the sensor error evaluation, but also strongly relies on the selected sensor error model. The best performance could be achieved only when the essential components of sensor errors are included and compensated. Two new sensor error models, with the concerns about sensor acceleration measurement biases and sensor attachment misalignment, have been developed. The performance of these two error models were evaluated in the shank-mounted IMU-based walking speed/inclination estimation algorithm with a comparison of an existing error model. The treadmill walking experiment, conducted at both level and incline conditions, demonstrated the importance of sensor error model selection on the spatio-temporal gait parameter estimation performance. Accurate walking inclination estimation was made possible with a newly developed sensor error model.

IMU-based ambulatory walking speed estimation in constrained treadmill and overground walking.

This study evaluated the performance of a walking speed estimation system based on using an inertial measurement unit (IMU), a combination of accelerometers and gyroscopes. The walking speed estimation algorithm segments the walking sequence into individual stride cycles (two steps) based on the inverted pendulum-like behaviour of the stance leg during walking and it integrates the angular velocity and linear accelerations of the shank to determine the displacement of each stride. The evaluation was performed in both treadmill and overground walking experiments with various constraints on walking speed, step length and step frequency to provide a relatively comprehensive assessment of the system. Promising results were obtained in providing accurate and consistent walking speed/step length estimation in different walking conditions. An overall percentage root mean squared error (%RMSE) of 4.2 and 4.0% was achieved in treadmill and overground walking experiments, respectively. With an increasing interest in understanding human walking biomechanics, the IMU-based ambulatory system could provide a useful walking speed/step length measurement/control tool for constrained walking studies.

Ambulatory running speed estimation using an inertial sensor.

Techniques have been developed to analyze walking gait using accelerometer and gyroscope data from miniature inertial measurement units (IMU), but few attempts have been made to use similar approaches for running gait. The purpose of this study was to develop an algorithm capable of estimating running speed using a single shank-mounted IMU. Raw acceleration and angular velocity were recorded from an IMU sensor attached on the lateral side of the shank in the sagittal plane and a method of reliably detecting the shank vertical and the minimal shank velocity gait event was used to segment a running sequence into individual strides. Through integration, the orientation of the shank segment was determined and an estimate of stride-by-stride running speed was calculated by integrating the acceleration data. The algorithm was verified using data collected from a group of seven volunteers running on a treadmill at speeds between 2.50 m/s and 3.50 m/s. Over the entire speed range, the estimation results gave a percentage root mean square error (%RMSE) of approximately 4.10%. With the accurate estimation capability and portability, the use of the proposed system in outdoor running gait analysis is promising.

Mechanical energy transfers across lower limb segments during stair ascent and descent in young and healthy older adults.

Older adults present with altered movement patterns during stair negotiation although the extent to which modifications in pattern and speed influence mechanical efficiency is unknown. This study evaluated mechanical energy transfers attributed to active force production during stair negotiation in young and older adults to provide insight into age-related changes in mechanical efficiency. Secondary analysis on data obtained from 23 young (23.7±3.0 years) and 32 older adults (67.0±8.2 years) during self-paced stair ascent and descent was conducted. Mechanical energy expenditures (MEE) during concentric transfer, eccentric transfer and no-transfer phases were determined for the ankle, knee and hip power profiles in the sagittal plane. Mechanical energy compensations (MEC) were also determined at each joint. During ascent, MEEs were similar for young and older adults although older adults compensated ankle muscles to a lesser extent during concentric muscle action. Controlling for cadence eliminated this difference. During descent, older adults demonstrated lower energy expenditures at the ankle and hip and similar expenditures at the knee compared to young adults. Changes in joint MEE in the older group resulted in reduced energy compensation at the ankle during concentric and eccentric activity and at the knee during eccentric activity. These age-related differences in mechanical energy transfers and related adjustments in MEC were not a function of the slower cadence in older adults and suggest a loss in mechanical efficiency. These results provide a benchmark against which physical impairments in older adults may be explored.

A concurrent comparison of inertia sensor-based walking speed estimation methods.

This study performed a concurrent comparison of two walking speed estimation methods using shank- and foot-mounted inertial measurement units (IMUs). Based on the cyclic gait pattern of the stance leg during walking, data was segmented into a series of individual stride cycles. The angular velocity and linear accelerations of the shank and foot over each of these cycles were then integrated to determine the walking speed. The evaluation was performed on 10 healthy subjects during treadmill walking where known treadmill speeds were compared with the estimated walking speeds under normal and toe-out walking conditions. Results from the shank-mounted IMU sensor yielded more accurate walking speed estimates, with a maximum root mean square estimation error (RMSE) of 0.09 m/s in normal walking and 0.10 m/s in toe-out conditions; while the foot-mounted IMU sensors yielded a maximum RMSE of 0.14 m/s in normal walking and 0.26 m/s in toe-out conditions. Shank-mounted IMU sensors may prove to be of great benefit in accurately estimating walking speeds in patients whose gait is characterized by abnormal foot motions.