5 research outputs found
MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning
Mobile phones and other electronic gadgets or devices have aided in
collecting data without the need for data entry. This paper will specifically
focus on Mobile health data. Mobile health data use mobile devices to gather
clinical health data and track patient vitals in real-time. Our study is aimed
to give decisions for small or big sports teams on whether one athlete good fit
or not for a particular game with the compare several machine learning
algorithms to predict human behavior and health using the data collected from
mobile devices and sensors placed on patients. In this study, we have obtained
the dataset from a similar study done on mhealth. The dataset contains vital
signs recordings of ten volunteers from different backgrounds. They had to
perform several physical activities with a sensor placed on their bodies. Our
study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree,
Random Forest, and Logistic Regression) to analyze and predict human health
behavior. XGBoost performed better compared to the other machine learning
algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in
specificity, and 99.66% in F1 score. Our research indicated a promising future
in mhealth being used to predict human behavior and further research and
exploration need to be done for it to be available for commercial use
specifically in the sports industry.Comment: 6, Accepted by 2nd International Seminar on Machine Learning,
Optimization, and Data Science (ISMODE
A Wearable Fall Detection System based on LoRa LPWAN Technology
Several technological solutions now available in the
market offer the possibility of increasing the independent life
of people who by age or pathologies otherwise need assistance.
In particular, internet-connected wearable solutions are of considerable interest, as they allow continuous monitoring of the
user. However, their use poses different challenges, from the real
usability of a device that must still be worn to the performance
achievable in terms of radio connectivity and battery life. The
acceptability of a technology solution, by a user who would still
benefit from its use, is in fact often conditioned by practical
problems that impact the person’s normal lifestyle. The technological choices adopted in fact strongly determine the success
of the proposed solution, as they may imply limitations both
to the person who uses it and to the achievable performance.
In this document, targeting the case of a fall detection sensor
based on a pair of sensorized shoes, the effectiveness of a real
implementation of an Internet of Things technology is examined.
It is shown how alarming events, generated in a metropolitan
context, are effectively sent to a supervision system through
Low Power Wide Area Network technology without the need
for a portable gateway. The experimental results demonstrate
the effectiveness of the chosen technology, which allows the user
to take advantage of the support of a wearable sensor without
being forced to substantially change his lifestyle
Feature extraction and feature selection in smartphone-based activity recognition
Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance
Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool
Computer programming and IoT are the key skills required in Industrial
Revolution 4.0 (IR4.0). The industry demand is very high and therefore related
students in this field should grasp adequate knowledge and skill in college or university
prior to employment. However, learning technology related subject without
applying it to an actual hardware can pose difficulty to relate the theoretical knowledge
to problems in real application. It is proven that learning through hands-on
activities is more effective and promotes deeper understanding of the subject matter
(He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education:
Case study of a modern technology infused courseware for embedded system
course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an
integrated learning tool that combines learning of computer programming and IoT
control for an industrial liquid filling system model is developed and tested. The
integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the
IoT application. The system set-up is pre-designed for semi-automation liquid filling
process to enhance hands-on learning experience but can be easily programmed for
full automation. Overall, it is a user and cost friendly learning tool that can be developed
by academic staff to aid learning of IoT and computer programming in related
education levels and field