Abstract
A lot of real-life mobile sensing applications are becoming available nowadays. The traditional approach for activity recognition employs machine learning algorithms to learn from collected data from smartphpne and induce a model. The model generation is usually performed offline on a server system and later deployed to the phone for activity recognition. In this paper, we propose a new hybrid classification model to perform automatic recognition of activities using built-in embedded sensors present in smartphones. The proposed method uses a trick to classify the ongoing activity by combining Weighted Support Vector Machines (WSVM) model and Hidden Markov Model (HMM) model. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our proposed method outperforms the state-of-the-art on a large benchmark dataset.
Highlights
Embedded Sensors are ubiquitous and are becoming sophisticated by nature
They demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for Weighted Support Vector Machines (WSVM) approach, and illustrate how the proposed method outperforms the state-ofthe-art on a large benchmark dataset
Classification stage: These algorithms are tested under MATLAB environment and the Weighted Support Vector Machine (SVM) algorithm is tested with an implementation from LibSVM (Hsu et al, 2011) with Gaussian kernel is used for all the datasets
Summary
Embedded Sensors are ubiquitous and are becoming sophisticated by nature. This has been changing people’s daily life and has opened the doors for many interesting data mining applications. Human activity recognition (HAR) is a research domain behind many applications on smartphone such as health monitoring, fall detection, context-aware mobile applications, human survey system, home automation, etc. Sensors used are generally contact switches to measure open-close states of doors and cupboards; pressure mats to measure sitting on couch or lying in bed; mercury contacts for movement of objects such as drawers; passive infrared sensors to detect motion in a specific area and float sensors to measure the toilet being flushed
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