A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, ‘Corner’, has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation.

Education and Corporeality: Contributions from the Philosophy of Sport

Corporeality is a subject strongly present in educational discussion nowadays. The purpose of this paper is to present an outline of issues we may address from the philosophy of sport that could foster a fruitful dialogue with the philosophy of education. It is understood that the philosophy of education can benefit from reflections on corporeality and human movement, namely from sports and games. Initially, the article introduces the philosophy of sport as a field of study that addresses reflections on human movement from sports and games. They highlight elements that are not specific to such practices and foster reflexions on different areas. Afterwards, it explores the experience of corporeality and the dialogical dimension of human movement based on Merleau-Ponty's phenomenology. Human movement indicates a unique way of being communicative. Finally, it presents some reflections on playing games as an experience that helps us think about our relationships with others and the environment. From this perspective it is possible to seek some critical features to understand education in the experience of human movement, namely from playing games, such as experience, dialogue, and expressiveness. Thoughts on human movements may reinforce the role corporeality plays in education as a collective experience and the recognition of the body's expressive potential in constructing knowledge.

The Association of Fatigue With Decreasing Regularity of Locomotion During an Incremental Test in Trained and Untrained Healthy Adults

Fatigue is a key factor that affects human motion and modulates physiology, biochemistry, and performance. Prolonged cyclic human movements (locomotion primarily) are characterized by a regular pattern, and this extended activity can induce fatigue. However, the relationship between fatigue and regularity has not yet been extensively studied. Wearable sensor methodologies can be used to monitor regularity during standardized treadmill tests (e.g., the widely used Bruce test) and to verify the effects of fatigue on locomotion regularity. Our study on 50 healthy adults [27 males and 23 females; <40 years; five dropouts; and 22 trained (T) and 23 untrained (U) subjects] showed how locomotion regularity follows a parabolic profile during the incremental test, without exception. At the beginning of the trial, increased walking speed in the absence of fatigue is associated with increased regularity (regularity index, RI, a. u., null/unity value for aperiodic/periodic patterns) up until a peak value (RI = 0.909 after 13.8 min for T and RI = 0.915 after 13.4 min for U subjects; median values, n. s.) and which is then generally followed (after 2.8 and 2.5 min, respectively, for T/U, n. s.) by the walk-to-run transition (at 12.1 min for both T and U, n. s.). Regularity then decreases with increased speed/slope/fatigue. The effect of being trained was associated with significantly higher initial regularity [0.845 (T) vs 0.810 (U), p < 0.05 corrected], longer test endurance [23.0 min (T) vs 18.6 min (U)], and prolonged decay of locomotor regularity [8.6 min (T) vs 6.5 min (U)]. In conclusion, the monitoring of locomotion regularity can be applied to the Bruce test, resulting in a consistent time profile. There is evidence of a progressive decrease in regularity following the walk-to-run transition, and these features unveil significant differences among healthy trained and untrained adult subjects.

Complexities of Socio-Labor Integration in Chile: Migrating Colombian Women’s Experiences

The objective of the article is to understand Afro-Colombian women’s emotional experiences of the migratory process, and their labor insertion in Chilean territory. The Antofagasta region is one of the doors that connects Chile with its neighbors; at the same time, it is a national territory that is linked to important economic and human movements due to its mining activity. In the analysis of the data collected through of group and individual interviews conducted in the city of Antofagasta, we found experiences of xenophobia, labor abuse, discrimination, prejudices, and stereotypes articulated, along with the tendency of Chilean culture to value European traits over native Latin American traits.

Classification of movements based on wearable device data in biometric authentication

The paper proposes a neural network algorithm for classifying human movements according to the accelerometer data, which is located in a mobile device. Intelligent algorithms for classifying movement types (single step, walking, walking on stairs, running) are considered on 9 types of different movements that a person performs in everyday life. The developed algorithm is proposed to be used in biometric authentication systems based on mobile phone data.