Stair climbing

About: Stair climbing is a research topic. Over the lifetime, 1610 publications have been published within this topic receiving 30504 citations.

In vivo knee forces after total knee arthroplasty

Abstract: Tibial forces were measured in vivo during the first year after total knee arthroplasty in a 66 kg, 80-year-old man. Forces were measured during activities of daily living, rehabilitation, and exercise. Peak tibial forcesrecorded during walking increased up to 12 months postoperatively (2.8 times body weight). Tibial forces correlated with increasing speed during treadmill walking. Rising from a chair generated peak forces of 2.6 times body weight. Stair descent generated higher peak forces than stair ascent (3.3 versus 2.9 times body weight, respectively). Exercising on a stair-climbing machine generated forces close to two times body weight whereas stationary bicycling generated even lower forces, near one times body weight. In general, the tibial forces recorded during walking and stair climbing were lower than most predicted values. These measurements can be used to validate in vitro and mathematical models of the knee. This should lead to refined surgical techniques and to enhanced prosthetic designs that will improve patient function, patient quality of life, and longevity of total knee arthroplasty implants.

Assessing Gait Patterns of Healthy Adults Climbing Stairs Employing Machine Learning Techniques

Abstract: So far, stair climbing has not been studied as extensively as gait has, although the significance of the prevention of falling on stairs has been well recognized. Based on acceleration data taken from 25 healthy subjects climbing up and down a set of 13 stairs with an accelerometer placed on the lumbo-sacral joint, this paper aims to assess gait patterns of younger and older adults climbing stairs using a machine learning approach. A total of 14 gait features were extracted and analyzed. The performance of six representative classification models: Multilayer Perceptron MLP, KStar, Support Vector Machine SVM, Naive Bayesian NB, C4.5 Decision Trees, and Random Forests were evaluated in terms of their ability to discriminate between younger and older adults climbing up- and downstairs. MLP was found to provide the highest accuracy for classification. Accuracy of 95.7% was found for classifying a subject walking either up or down the stairs and an accuracy of 80.6% for classifying whether the subject was younger or older. An evaluation of individual features showed poor performance of classification for younger and older subjects climbing up- and downstairs, and in most cases failed to distinguish between the two classes. To access which set of features derived from a triaxial accelerometer can better describe the performance differences between younger and older adults climbing up- and downstairs, two feature selection algorithms, sequential feature selection and correlation-based feature selection, were implemented. Results show that 10 features derived from correlation-based feature selection were able to produce a 96.8% accuracy for classification between subjects climbing up and down. A subset of seven features achieved a performance of 84.9% accuracy for classification between younger and older subjects.

Development of a soft knee exosuit with twisted string actuators for stair climbing assistance

Abstract: Stair climbing remains one of the toughest tasks of the elderly in activities of daily livings (ADLs). However, most of the existing stair climbing assistance devices are large and heavy, such as the automatic stair climbing wheelchair, the rigid exoskeleton and so on. They all lack applicability in the daily life. Based on this starting point, in this paper, a soft knee suit (Hitexosuit) has been developed to assist people climbing. It is a lightweight and wearable assistance suit for human lower extremity in climbing the stairs. This paper finds out the certain phase of stair climbing motion that requires the most muscle strength and locates the target muscle that contributes most when people climb the stairs through the biomechanics analysis. Furthermore, this research draws inspiration from the human muscles, which contract to generate force for knee extension and help people lift their body to go upstairs. Based on that bionic principle, a new type of artificial muscle is developed. It simulates human muscle contraction by shortening the length of steel wire to give extra strength to human lower extremity. And textiles have been adopted in the suit, soft straps and belts to make up the structure of the suit instead of rigid and heavy materials. In addition, to minimize the whole weight of the exosuit system further, we develop a lightweight and portable actuator using twisted strings. This twisted string actuator (TSA) weighs 390g and the whole exosuit weight is 3.5kg with two TSAs. The exosuit is tested with three different users and the mean assistance efficiency is 29.8%. It will improve the living quality of the aged by guaranteeing them more confidence in ADLs and self-caring.