Results from 3 different classifiers (K-means, Naive-Bayes, Neural Network) are compared to identify the sensor modality and classification algorithm that ...
In this paper, we implement and compare the accuracy of 3 simple classification methods: k-means (a randomly initialised but deterministic algorithm), Naıve- ...
In this paper light, accelerometer and orientation sensor measurements are recorded for 4 different phone use cases and results from 3 different classifiers
The onboard accelerometer is found to be the sensor modality with highest accuracy across all the classifiers, and the neural network is identified as being ...
In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate ...
Comparison of classifiers for use case detection using onboard ...
colab.ws › ITNAC55475.2022.9998423
Comparison of classifiers for use case detection using onboard smartphone sensors. Imran Moez Khan 1. ,. Sun Shuai 1. ,. Wayne S T Rowe 1. ,. Andrew Thompson 2.
This paper presents a wireless body area network platform that performs physical activities recognition using accelerometers, biosignals and smartphones.
Missing: case onboard
Smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields.
Our sensor analytics pipeline automatically learns to distinguish between real phone handling and other types of phone movements.
The most commonly-used classifiers are decision tree, support vector machine (SVM), K-nearest neighbor (KNN) and naive Bayes. Some of the other implemented ...