118 research outputs found
A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors
Due to the many beneficial effects on physical and mental health and strong association with many fitness and rehabilitation programs, physical activity (PA) recognition has been considered as a key paradigm for internet of things (IoT) healthcare. Traditional PA recognition techniques focus on repeated aerobic exercises or stationary PA. As a crucial indicator in human health, it covers a range of bodily movement from aerobics to anaerobic that may all bring health benefits. However, existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we attempt to detect more gym physical activities (GPAs) in addition to traditional PA using acceleration, A two layer recognition framework is proposed that can classify aerobic, sedentary and free weight activities, count repetitions and sets for the free weight exercises, and in the meantime, measure quantities of repetitions and sets for free weight activities. In the first layer, a one-class SVM (OC-SVM) is applied to coarsely classify free weight and non-free weight activities. In the second layer, a neural network (NN) is utilized for aerobic and sedentary activities recognition; a hidden Markov model (HMM) is to provide a further classification in free weight activities. The performance of the framework was tested on 10 healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/ and compared with some typical classifiers. The results indicate the proposed framework has better performance in recognizing and measuring GPAs than other approaches. The potential of this framework can be potentially extended in supporting more types of PA recognition in complex applications
Human activity data discovery based on accelerometry
Dissertation to Obtain Master Degree in
Biomedical Engineerin
Window Selection Impact in Human Activity Recognition
Signal segmentation is usually applied in the pre-processing step to make the data analysis easier. Windowing approach is commonly used for signal segmentation. However, it is unclear which type of window should be used to get optimum accuracy in human activity recognition. This study aimed to evaluat e which window type yields the optimum accuracy in human activity recognition. The acceleration data of walking, jogging, and running were collected from 20 young adults. Then, the recognition accuracy of each window types is evaluated and compared to determine the impact of window selection in human movement data. From the evaluation, the overlapping 75% window with 0.1 s length provides the highest accuracy with mean, standard deviation, maximum, minimum, and energy as the features. The result of this study could be used for future researches in relation to human activity recognition. 
Activity and Health Status Monitoring System
Physical activity monitoring represents an important tool in supporting/encouraging vulnerable persons in their struggle to recover from surgery or long term illness promoting a healthy lifestyle. The paper proposes a smart, low power activity monitoring platform capable to acquire data from 4 inertial sensor modules placed on human body, temporarily store it on a mobile phone for real time data display or on a server for long term data analysis
Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches
Physical activity recognition (PAR) using wearable devices can provide valued
information regarding an individual's degree of functional ability and
lifestyle. In this regards, smartphone-based physical activity recognition is a
well-studied area. Research on smartwatch-based PAR, on the other hand, is
still in its infancy. Through a large-scale exploratory study, this work aims
to investigate the smartwatch-based PAR domain. A detailed analysis of various
feature banks and classification methods are carried out to find the optimum
system settings for the best performance of any smartwatch-based PAR system for
both personal and impersonal models. To further validate our hypothesis for
both personal (The classifier is built using the data only from one specific
user) and impersonal (The classifier is built using the data from every user
except the one under study) models, we tested single subject validation process
for smartwatch-based activity recognition.Comment: 15 pages, 2 figures, Accepted in CVC'1
Optimal Accelerometer Placement for Fall Detection of Rehabilitation Patients
The development of health monitoring system using wearable sensor has lots of potential in the field of rehabilitation and gained lots of attention in the scientific community and industry. The aim and motivation in this field are to focus on the application of wearable technology to monitor elderly or rehab patients in home-based settings to reduce resources and development cost. The wearable sensor such as accelerometer used to emphasise the clinical applications of fall detection during rehabilitation treatment. This paper is intended to determine the optimal sensor placement especially for lower limb activity during rehabilitation exercise. Accelerometer data were collected from three different body locations (hip, thigh, and foot). The lower limb activities involve normal movements such as walking, lifting, sit-to-stand, and stairs. Other unexpected activity such as falls might occur during normal lower limb exercise movement. Then, acceleration data for various lower limbs activities was classified using k-NN and SVM classifier. The result found that the hip was the best location to record data for lower limb activities including when fall occurs
Low Energy Physical Activity Recognition System on Smartphones
An innovative approach to physical activity recognition based on the use
of discrete variables obtained from accelerometer sensors is presented. The system first
performs a discretization process for each variable, which allows efficient recognition of
activities performed by users using as little energy as possible. To this end, an innovative
discretization and classification technique is presented based on the 2 distribution.
Furthermore, the entire recognition process is executed on the smartphone, which determines
not only the activity performed, but also the frequency at which it is carried out. These
techniques and the new classification system presented reduce energy consumption caused
by the activity monitoring system. The energy saved increases smartphone usage time to
more than 27 h without recharging while maintaining accuracy.Ministerio de Economía y Competitividad TIN2013-46801-C4-1-rJunta de Andalucía TIC-805
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