A novel first responders location tracking system: Architecture and functional requirements

Tedesco, Salvatore; Khodjaev, Jasurbek; O’Flynn, Brendan

doi:10.1109/mms.2015.7375416

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…In [4], we compared kNN, SVM and GBT using data describing 17 human activities in the context of an emergency first responders monitoring system [5] developed as part of the European SAFESENS (Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants) project. The data-set consisted of 65 features which had been extracted from acceleration and angular velocity signals collected by a wearable IMU that was attached to the chest and equipped with barometer, 3D accelerometer, and 3D gyroscope.…”

Section: Methodsmentioning

confidence: 99%

Sensor and feature selection for an emergency first responders activity recognition system

et al. 2017

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Access to the full text of the published version may require a subscription. Abstract-Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients' recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms. Rights

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Section: Methodsmentioning

confidence: 99%

Sensor and feature selection for an emergency first responders activity recognition system

et al. 2017

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“…The data used in our experiments were obtained from a wearable inertial measurement unit (IMU) which is a component of the indoor localisation [51,52] and status monitoring system for emergency first responders developed by the SAFESENS project [53]. These IMUs are equipped with a high-performance low-power 168 MHz 32-bit microprocessor with 1MB of flash memory and 192 KB + 4 KB of random access memory (RAM), a bluetooth low-energy (BLE) communication module, a rechargeable battery, sensors for barometric pressure, humidity, temperature (internal and external), and a tri-axial accelerometer, gyroscope and magnetometer.…”

Section: Data Descriptionmentioning

confidence: 99%

Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition

Scheurer

Tedesco

Brown

et al. 2020

Sensors

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Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.

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“…Some of these accidents could be averted, and many ameliorated, if firefighters (and other first responders) had better information about the situation on the ground as it unfolds. The SAFESENS (Sensor Technologies for Enhanced Safety and Security of Buildings and its Occupants) project [2] is developing a location-tracking and monitoring system for firefighters and other first responders which will make that information available to them.…”

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confidence: 99%

Human activity recognition for emergency first responders via body-worn inertial sensors

Scheurer

Tedesco

Brown

et al. 2017

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Access to the full text of the published version may require a subscription. Abstract-Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first responders had better information about the information on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbors (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms the other two approaches for three of these four problems, and that it leads to a generalisation error that is more balanced across the target activities than SVM. Rights

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A novel first responders location tracking system: Architecture and functional requirements

Cited by 5 publications

References 4 publications

Sensor and feature selection for an emergency first responders activity recognition system

Sensor and feature selection for an emergency first responders activity recognition system

Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition

Human activity recognition for emergency first responders via body-worn inertial sensors

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