The Best You: Gym Based Machine Learning Application

Use of artificial intelligence (AI) and machine learning is rapidly becoming more widespread in the 21st century. Both are quickly emerging increasingly vital aspects of today's standard exercise routines. Artificial intelligence has become inextricably linked to health and fitness. Experts in the field of technology believe that AI will solve all problems. When it comes to fitness, it has the ability to empower the app by drastically increasing engagement, which may lead to long-term income. In other words, it has the potential to make money. Apps that are equipped with AI have the potential to provide consumers a wide range of benefits. It is feasible for a person who is interested in fitness to save money because an artificial intelligence fitness trainer is more cost-effective than a human trainer. On the other hand, joining a gym may be cost prohibitive or just not doable given our hectic schedules. Aside from that, using fitness software that is powered by AI might make working out more fascinating and fun. In this section, we will discuss some of the best fitness applications that are powered by AI and machine learning models. This app creates unique training plans for each user using artificial intelligence This app was originally designed exclusively for use in gyms, but it recently changed its focus to meet the rising demand for at-home exercise. Simply put, FitnessAI pushes users to effectively build muscle every time they exercise by optimizing their weight lifting sets, repetitions, and weights for each activity. The main purpose of proposing this application system is to provide gym-goers with the right information at the right time, preventing them from taking the wrong supplements to maintain their body well

Augmenting Vision-Based Human Pose Estimation with Rotation Matrix

Fitness applications are commonly used to monitor activities within the gym, but they often fail to automatically track indoor activities inside the gym. This study proposes a model that utilizes pose estimation combined with a novel data augmentation method, i.e., rotation matrix. We aim to enhance the classification accuracy of activity recognition based on pose estimation data. Through our experiments, we experiment with different classification algorithms along with image augmentation approaches. Our findings demonstrate that the SVM with SGD optimization, using data augmentation with the Rotation Matrix, yields the most accurate results, achieving a 96% accuracy rate in classifying five physical activities. Conversely, without implementing the data augmentation techniques, the baseline accuracy remains at a modest 64%.Comment: 24 page

IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off

The microgravity exposure that astronauts undergo during space missions lasting up to 6 months induces biochemical and physiological changes potentially impacting on their health. As a countermeasure, astronauts perform an in-flight training program consisting in different resistive exercises. To train optimally and safely, astronauts need guidance by on-ground specialists via a real-time audio/video system that, however, is subject to a communication delay that increases in proportion to the distance between sender and receiver. The aim of this work was to develop and validate a wearable IMU-based biofeedback system to monitor astronauts in-flight training displaying real-time feedback on exercises execution. Such a system has potential spin-offs also on personalized home/remote training for fitness and rehabilitation. 29 subjects were recruited according to their physical shape and performance criteria to collect kinematics data under ethical committee approval. Tests were conducted to (i) compare the signals acquired with our system to those obtained with the current state-of-the-art inertial sensors and (ii) to assess the exercises classification performance. The magnitude square coherence between the signals collected with the two different systems shows good agreement between the data. Multiple classification algorithms were tested and the best accuracy was obtained using a MultiLayer Perceptron (MLP). MLP was also able to identify mixed errors during the exercise execution, a scenario that is quite common during training. The resulting system represents a novel low-cost training monitor tool that has space application, but also potential use on Earth for individuals working-out at home or remotely thanks to its ease of use and portability

Motion Coach

People find it difficult to do tasks which are related to maintaining correct posture. For activities related to performing a posture routine correctly by oneself, unfortunately there aren’t many systems out there which help people to do so and provide feedback on what went wrong and how they could improve it. There are limited options for people who prefer to learn the activity by themselves, at their convenient location. YouTube [1] tutorials are a predominant option in this space. Hence this Master’s report presents Motion Coach, the first system which addresses these issues by guiding the user through a pose routine by breaking it into sequential stages, providing real-time feedback from the convenience of a user's laptop. Two assessments evaluated Motion Coach. In the user study 93.75% of the people felt that Motion Coach made the motion activity easier when compared to learning it from manual methods (​Figure 14(a)​ ). 100% of the users felt the voice instructions with corrective feedback, static pose images made learning the activity much easier (​Figure 14(a)​ ). 100% of the users agreed that they would like to use Motion Coach again, for learning other activities if possible (​Figure 15​). Users on average learned the activity 49% faster when compared to manual methods (​Table 2​). Motion Coach could effectively guide participants to do the motion activity (a pose routine) correctly and also save time. It needs no additional hardware, making it suitable to use for the majority of people. These promising results suggest that Motion Coach could serve as a starting point for future enhancements for applying it to physiotherapy [2], teaching golf swings, learning ballet or weight training

An All-in-One mHealth Application: #Beats – Your health mate

Màster en Gestió de Continguts Digitals, Facultat d'Informació i Mitjans Audiovisuals, Universitat de Barcelona i UPF, curs 2019-2020. Tutor: Dr. Cristóbal Urbano. UBBy exploring the current situation of the mHealth market in Spain, and the feasibility of the open-source framework, this article looks forward to developing an all-in-one mHealth application with the concept of Mini Programs/ Instant App. It can integrate the healthcare resources and provide users with more experience of instant services without a complicated installation process. It also strengthens the protection of personal information and privacy. In the meanwhile, by applying the methodology of Rapid Prototyping, a user interface of this app, Beats, will be presented to visualize the above concepts. It may be a revolution for medical providers, doctor-patient relationships, public health care systems, and even the entire healthcare system

2023 JSU Student Symposium Proceedings

https://digitalcommons.jsu.edu/ce_jsustudentsymp_2023/1062/thumbnail.jp

Embodied Autoethnography

In the past few decades, scholars have begun to combine research and personal experience, exploring the self through autoethnography. This thesis is a reflexive, arts-based autoethnographic study that investigates body, female body image, and identity. Though autoethnography has several subgenres (e.g., critical, performative), this thesis aligns most closely with embodied autoethnography. With this embodied autoethnography, I invite readers—you—inside several pivotal experiences in my life. Combining personal narrative and others’ research, I endeavor to understand changes in body image and identity in some of the most transformative experiences in my life. Specifically, I seek to address: (a) How do life-altering events impact a woman’s body image and identity? and (b) How does sharing my embodied autoethnographic narratives impact my “self” and others

Event-driven Middleware for Body and Ambient Sensor Applications

Continuing development of on-body and ambient sensors has led to a vast increase in sensor-based assistance and monitoring solutions. A growing range of modular sensors, and the necessity of running multiple applications on the sensor information, has led to an equally extensive increase in efforts for system development. In this work, we present an event-driven middleware for on-body and ambient sensor networks allowing multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the required data representation which lifts the burden of adapting the application with respect to the connected sensors off the developer's shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the system's adaptation to future applications and sensors at run-time as well as reducing the number of connected sensors. Open communication channels distribute sensor information to all interested applications. In addition to that, application-specific event channels are introduced that provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the middleware and the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the open and application-specific event channels with respect to CPU utilization, delivery ratio, and memory usage. In addition to the middleware approach, four (preventive) health care applications are presented. They take advantage of the mediation between sensors and applications and highlight the system's capabilities. By connecting body sensors for monitoring physical and physiological parameters as well as ambient sensors for retrieving information about user presence and interactions with the environment, full-fledged health monitoring examples for monitoring a user throughout the day are presented. Vital parameters are gathered from commercially available biosensors and the mediator device running both the middleware and the application is an off-the-shelf smart phone. For gaining information about a user's physical activity, custom-built body and ambient sensors are presented and deployed

THE EFFECTS OF A 7-WK HEAVY ELASTIC BAND AND WEIGHTED CHAIN PROGRAM ON UPPER BODY STRENGTH AND UPPER BODY POWER IN A SAMPLE OF DIVISION 1-AA FOOTBALL PLAYERS

Within recent years, strength training involving the modalities of heavy elastic bands and weighted chains has received widespread recognition and increased popularity. PURPOSE: The purpose of this study was to explore the effects of a seven week heavy elastic band and weighted chain program on maximum muscular strength and maximum power in the bench press exercise. METHODS: Thirty six (n=36) healthy males 18-30 years old from the Robert Morris University football team volunteered to participate in this study. During the first week, predicted one repetition maximum (1RM) bench press and a five repetition (5RM) maximum speed bench press tests were conducted. Subjects were randomly divided into three groups (n=12): elastic band (EB), weighted chain (WC) and control (C). Subjects were oriented to the elastic band (EB) and chain weighted (WC) bench press prior to pre testing. During weeks 2 through 8 of the study, subjects were required to follow the resistance training program designed for using the EB and WC for seven weeks. All other components of normal spring training and conditioning remained the same. Means and standard deviations of the predicted 1RM bench press and 5RM speed bench press were computed in the first and ninth week of the program. A two factor (method X time) analysis was applied to identify significant differences between the training groups. Statistical significance was set at α = 0.05. RESULTS: Results indicated a significant time (*p < 0.05), but no group effect for both predicted 1RM (kg) and 5RM peak power tests (watts). Although not significant, results did show greater improvements in the EB and WC groups compared to control when the two highest and greatest values were selected regarding peak power. CONCLUSION: This study suggests that the use of EB and WC in conjunction with a general seven week off season strength and conditioning program can increase overall maximum upper body strength in a sample of Div 1-AA football players. PRACTICAL APPLICATION: The implementation of heavy elastic bands and weighted chains into a strength and conditioning regimen may result in potential gains in muscular strength and power. These types of training modalities add a unique training style and more flexibility in respect to exercise prescription for athletes and strength practitioners