Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions

Maurer, U.; Smailagic, Asim; Siewiorek, D.P.; Deisher, Michael

doi:10.1109/bsn.2006.6

Cited by 407 publications

(236 citation statements)

References 5 publications

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“…Battery lifetime estimations in a indicative scenario demonstrate the dependency of the battery lifetime to the configuration settings of the accelerometers (estimations range from weeks to years). For example, assuming 210 mAh battery capacity, a configuration used in [16] yields a battery lifetime estimation of 240 days.…”

Section: Resultsmentioning

confidence: 99%

“…Social studies [27][3] have shown the importance of wearable devices being comfortable and not intrusive to the daily life activities. In [16], the authors assess various body positions and present comparison results in which the wrist ranks high in all the considered activities in terms of classification accuracy. The core component is a nRF51822 system-on-chip (SoC) which incorporates a ARM Cortex M0 microcontroller unit (MCU), 32KB of RAM, 256KB of non-volatile flash memory, and a BLE radio (a comparison study of BLE and ZigBee can be found in [20]).…”

Section: Related Workmentioning

confidence: 99%

“…At low sampling frequencies, instead, the idle consumption becomes increasingly more important. In [16], the authors use accelerometers with 8-bit resolution to perform activity classification. Experimenting with different sampling frequencies, the authors show that the performance of the classifier reaches a high level at approximately 10 Hz with only marginal improvement at higher frequencies.…”

Section: Energy Consumption and Battery Lifetime Estimationsmentioning

confidence: 99%

See 2 more Smart Citations

SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

Fafoutis

Janko

Mellios

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. In this paper, we present SPW-1; a low-profile versatile wearable activity tracker that employs two ultra-low-power accelerometers and relies on Bluetooth Low Energy (BLE) for wireless communication. Aiming for a low maintenance system, SPW-1 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-1 can fully cover a room and -in most cases -the adjacent room, as well. SPW-1 is a research platform that is aimed to be used both as a data collecting tool for health-oriented studies outside the laboratory, but also for research on wearable technologies and body-centric communications. As a result, SPW-1 incorporates versatile features, such as external sensor support, various powering options, and accelerometer configuration options that can support a wide range applications from kinematics to long-term activity recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Energy Consumption and Battery Lifetime Estimationsmentioning

confidence: 99%

See 1 more Smart Citation

SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

Fafoutis

Janko

Mellios

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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show abstract

“…A ``ewatch" device is placed in shirt pocket, trouser pocket, backpack etc. to recognize the common user activities, with the use of bi-axial accelerometer and light sensor by Maurer et al [16]. Choudhury et al [8] used a model consisting of seven different sensors to recognize activities.…”

Section: Related Workmentioning

confidence: 99%

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Liu¹,

Zhou²,

Shakya³

et al. 2018

IJMLC

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Abstract-Smartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs.

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“…Previous works have shown that, machine learning algorithms are efficient for human movement classification. Khan [4,5]. It was the main motivation to use those three machine learning methods in this study.…”

confidence: 99%

Feature Analysis to Human Activity Recognition

Sütő

Oniga²,

Pop

2016

INT J COMPUT COMMUN

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Human activity recognition (HAR) is one of those research areas whose importance and popularity have notably increased in recent years. HAR can be seen as a general machine learning problem which requires feature extraction and feature selection. In previous articles different features were extracted from time, frequency and wavelet domains for HAR but it is not clear that, how to determine the best feature combination which maximizes the performance of a machine learning algorithm. The aim of this paper is to present the most relevant feature extraction methods in HAR and to compare them with widely-used filter and wrapper feature selection algorithms. This work is an extended version of [1] a where we tested the efficiency of filter and wrapper feature selection algorithms in combination with artificial neural networks. In this paper the efficiency of selected features has been investigated on more machine learning algorithms (feed-forward artificial neural network, k-nearest neighbor and decision tree) where an independent database was the data source. The result demonstrates that machine learning in combination with feature selection can overcome other classification approaches.

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Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions

Cited by 407 publications

References 5 publications

SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Feature Analysis to Human Activity Recognition

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