A mobile system for sedentary behaviors classification based on accelerometer and location data

Ceron, Jesus D.; López, Diego M.; Ramı́rez, Gustavo

doi:10.1016/j.compind.2017.06.005

Cited by 21 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system can also be used to detect behavior patterns in the visitors by museum administrators. Ceron et al [ 37 ] used BLE beacons to monitor sedentary behaviors in in-home environments, correctly identifying the user activities and detecting continuously repeated sedentary behaviors. All these systems, and those based on ZigBee and Wi-Fi, used the Receive Signal Strength Indicator (RSSI) to indicate proximity.…”

Section: Related Workmentioning

confidence: 99%

Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

Aguilera

Aranda

Parralejo

et al. 2021

Sensors

View full text Add to dashboard Cite

Proximity-Based Indoor Positioning Systems (PIPSs) are a simple to install alternative in large facilities. Besides, these systems have a reduced computational cost on the mobile device of those users who do not continuously demand a high location accuracy. This work presents the design of an Acoustic Low Energy (ALE) beacon based on the emission of inaudible Linear Frequency Modulated (LFM) signals. This coding scheme provides high robustness to in-band noise, thus ensuring a reliable detection of the beacon at a practical range, after pulse compression. A series of experimental tests have been carried out with nine different Android devices to study the system performance. These tests have shown that the ALE beacon can be detected at one meter distance with signal-to-noise ratios as low as −12 dB. The tests have also demonstrated a detection rate above 80% for reception angles up to 50∘ with respect to the beacon’s acoustic axis at the same distance. Finally, a study of the ALE beacon energy consumption has been conducted demonstrating comparable power consumption to commercial Bluetooth Low Energy (BLE) beacons. Besides, the ALE beacon search can save up to 9% more battery of the Android devices than the BLE beacon scanning.

show abstract

Section: Related Workmentioning

confidence: 99%

Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

Aguilera

Aranda

Parralejo

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In this phase, the modeling techniques are selected and applied and, if necessary, their parameters are calibrated to improve their results. Based on the results obtained in a previous work in which the classification of sedentary behaviors was performed using acceleration and proximity data with BLE beacons [8], the classification algorithms used in this work are: J48, Ib1, SVM, Random Forest (RF), AdaBoostM1 (ABM1), and Bagging. The last three are ensemble algorithms and J48 was set as their base classifier.…”

Section: Modelingmentioning

confidence: 99%

“…The last three are ensemble algorithms and J48 was set as their base classifier. To perform the training and evaluation of the models, the application developed in the context of the work presented in [8] was adapted. That application, written in JAVA, incorporates the Waikato Environment for Knowledge Analysis (WEKA) library [9].…”

Section: Modelingmentioning

confidence: 99%

Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach

2018

View full text Add to dashboard Cite

Although there have been many studies aimed at the field of Human Activity Recognition, the relationship between what we do and where we do it has been little explored in this field. The objective of this paper is to propose an approach based on machine learning to address the challenge of the 1st UCAmI cup, which is the recognition of 24 activities of daily living using a dataset that allows to explore the aforementioned relationship, since it contains data collected from four data sources: binary sensors, an intelligent floor, proximity and acceleration sensors. The methodology for data mining projects CRISP-DM was followed in this work. To perform synchronization and classification tasks a java desktop application was developed. As a result, the accuracy achieved in the classification of the 24 activities using 10-fold-cross-validation on the training dataset was 92.1%, but an accuracy of 60.1% was obtained on the test dataset. The low accuracy of the classification might be caused by the class imbalance of the training dataset; therefore, more labeled data are necessary for training the algorithm. Although we could not obtain an optimal result, it is possible to iterate in the methodology to look for a way to improve the obtained results.

show abstract

“…Thus, smartphones are now paid attention as a potentially cost-efficient and low-burden way for tracking daily physical activity. [16][17][18][19] The aim of this cross-sectional study, therefore, was to identify the characteristics of indicators of SB, focusing on the examination of correlations, reliability, and validity of sedentary variables assessed by the smartphone app.…”

Section: Introductionmentioning

confidence: 99%

“…Smartphone devices could provide enough information to estimate sedentary time in daily life though, much evidence on its' availability for academic research would be needed. Thus, smartphones are now paid attention as a potentially cost‐efficient and low‐burden way for tracking daily physical activity …”

Section: Introductionmentioning

confidence: 99%

Identifying characteristics of indicators of sedentary behavior using objective measurements

Yamamoto

Matsuda

Matsukawa

et al. 2019

Journal of Occupational Health

View full text Add to dashboard Cite

show abstract

A mobile system for sedentary behaviors classification based on accelerometer and location data

Cited by 21 publications

References 17 publications

Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach

Identifying characteristics of indicators of sedentary behavior using objective measurements

Contact Info

Product

Resources

About