The nature of statistical learning theory
The nature of statistical learning theory
Mixing floating- and fixed-point formats for neural network learning on neuroprocessors
Microprocessing and Microprogramming
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
IEEE Pervasive Computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Review: Pervasive computing at scale: Transforming the state of the art
Pervasive and Mobile Computing
Nature inspiration for support vector machines
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Applications of support vector machines to speech recognition
IEEE Transactions on Signal Processing
The design of a neuro-microprocessor
IEEE Transactions on Neural Networks
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Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject's body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.