Simple and Complex Activity Recognition through Smart Phones

By Stefan Dernbach, Barnan Das, Narayanan Chatapuram Krishnan, Brian L. Thomas, Diane Joyce Cook. 2012 Eighth International Conference on Intelligent Environments, 2012

328 citations recorded by the source corpus.

Due to an increased popularity of assistive healthcare technologies activity recognition has become one of the most widely studied problems in technology-driven assistive healthcare domain. Current approaches for smart-phone based activity recognition focus only on simple activities such as locomotion. In this paper, in addition to recognizing simple activities, we investigate the ability to recognize complex activities, such as cooking, cleaning, etc. through a smart phone. Features extracted from the raw inertial sensor data of the smart phone corresponding to the user's activities, are used to train and test supervised machine learning algorithms. The results from the experiments conducted on ten participants indicate that, in isolation, while simple activities can be easily recognized, the performance of the prediction models on complex activities is poor. However, the prediction model is robust enough to recognize simple activities even in the presence of complex activities.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f35a83de13e974b90c8575a83edb18c1c3f816c3

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Window Size Impact in Human Activity Recognition (2014, methodology): “This way, in [13,14], the monitored volunteers are made to participate in the segmentation process by requiring them to set initial and end points in a handheld device.”
Human activity recognition from accelerometer data using Convolutional Neural Network (2017, methodology): “Various methods for detecting human activity using smartphone accelerometer sensors have been proposed [2].”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, methodology): “Although we use these two categories to group papers, not all works classify tasks only from either global or local activities, works such as [19]–[23] classify activities from both categories.”
A Public Domain Dataset for Human Activity Recognition using Smartphones (2013, result): “Similarly, other works have provided public data, such as [11] and [12].”
Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition (2016, methodology): “This paper is organized as follows: in Section II, we present related works on image annotation; in Section III, we detail the newly proposed MARS; in Section IV, we present the experimental results of the new approach applied to the naturalistic mobile devices-based human activity (NMHA) dataset;…”