Effect of crank length on biomechanical parameters and muscle activity during standing cycling.

This study investigated the effect of crank length on biomechanical parameters and muscle activity during standing cycling. Ten participants performed submaximal cycling trials on a stand-up bicycle using four crank lengths. Joint angles, moments, powers, and works of the lower limbs were calculated from motion data and pedal reaction forces. Electromyographic (EMG) data were recorded from gluteus maximus (GM), vastus medialis, rectus femoris, biceps femoris (BF), gastrocnemius medialis, soleus, and tibialis anterior, and used to obtain the integrated EMG. Statistical parametric mapping was employed to analyse the biomechanical parameters throughout the pedalling cycle. Knee and hip flexion angles and hip power increased at the initiation (0-20%) of pedalling with increasing crank length, while the BF and GM muscle activities increased during propulsion (20-40%). Additionally, increasing the crank length resulted in increased knee power absorption during upstroke phase (70-100%). Peak knee extension moment increased with decreasing crank length during propulsion, but the moment at a short crank length during propulsion was comparable to fast walking. Consequently, longer crank lengths require increased propulsion power by the lower limb muscles during standing cycling compared to shorter crank lengths. Therefore, shorter crank lengths are recommended for stand-up bicycles to avoid fatigue.

Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns.

Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.

A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram.

Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning.

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced.

Influence of folding mechanism of bicycles on their usability.

In this study, foldable bicycles were evaluated in terms of their usability. Four types of folding mechanisms were identified depending on the number of pivots and the pivot axis direction: single lateral pivot (SLP), single vertical pivot, dual lateral pivot, and combined vertical-lateral pivot. Next, four bicycles-one each of these four types-were selected as test specimens. Ten subjects performed folding and unfolding tasks on each of these bicycles, and three-dimensional body motions and ground reaction forces were measured. The maximum trunk flexion angles and maximum increments in the ground reaction force were used as governing parameters for evaluating the comfort level for each bicycle type. The SLP type provided the lowest upper body flexion and ground reaction force and was hence judged to be the most comfortable folding system. Hence, a promising type of easily foldable bicycle was proposed, thereby encouraging its incorporation into public transit systems.