A Case Study On Robustness Of Dynamic Time Warping For Activity Recognition Using Wearable Computers
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A Case Study on Robustness of Dynamic Time Warping for Activity Recognition Using Wearable Computers
We describe a body sensor system that detects human activities in real-time. The system consists of wearable computers known as sensor nodes (motes) that can sense information, process them and transmit the results to a Personal Device like Smart phone, PDA or Personal Computer. The motes are attached to different parts of the human body, namely waist and right thigh. Daily living activity monitoring is important in improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing for time-series pattern matching because of its robustness to variations in time domain and speed as opposed to other template matching methods such as Euclidean Distance. Despite of this flexibility, for the application of activity recognition, DTW can only find the similarity between template of a movement and the incoming samples, when the location and orientation of sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to false classifications. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand. To measure this performance of DTW, we need infinite closely spaced sensors which are impractical. To deal with this problem, we use the marker based optical motion capture system and generate inertial sensor data for different location and orientation on the body. We study the performance of the DTW under these conditions and determine the worst-case sensor location variations, the algorithm can accommodate.
Towards Robust & Realtime Human Activity Recognition Using Wearable Sensors
With the proliferation of smartphones and fitness bands that have various sensors such as accelerometers, wearable sensor-based Human Activity Recognition (HAR) systems have gained wide popularity and researchers have proposed numerous techniques for recognition of these activities. Human activity recognition has many applications particularly in health care, cognitive assistance, city planning, indoor localization and tracking, and human-computer interaction. Although there has been some progress, a practical robust HAR system remains elusive because the collected data are affected by several factors such as noise, data alignment, and other constraints. In addition, the variability in the sensing equipment and their displacement is a practical challenge for implementing HAR in real-world applications. This dissertation explores the twin problems of making wearable sensor-based HAR systems robust and real time. Towards enhancing the robustness of ML-based HAR systems, we adopt feature selection methods on time and frequency domain features and apply classifiers for evaluating the recognition performance. We show the effect of different feature sets on each of the classifiers and further demonstrate in our results the impact of decreasing the size of the training set on the accuracy of the classifiers. Towards building an Online HAR system, this thesis explores the concept of Shapelets to avoid complex feature extraction. We propose a procedure to find the most representative shapelet for each activity class based on time series distance metrics and dynamic time warping. Furthermore, we generate a personalized shapelet library database driven from users' activity time series. We evaluate the proposed algorithm and techniques using a dataset comprised of accelerometer readings of 77 individuals performing various activities such as walking/jogging on treadmill, walking on different surfaces, climbing stairs, and non-ambulatory activities. Our experiments demonstrate that by using selected features from the time and frequency domain, we can achieve higher accuracy rates if we limit the training and testing sets to specific age groups. Furthermore, while we mainly use a single hip-worn accelerometer sensor as our sensing device, we show our method could support any wearable accelerometer sensor.
Robust Human Activity Recognition Using Smartwatches and Smartphones
Abstract: Smart user devices are becoming increasingly ubiquitous and useful for detecting the user's context and his/her current activity. This work analyzes and proposes several techniques to improve the robustness of a Human Activity Recognition (HAR) system that uses accelerometer signals from different smartwatches and smartphones. This analysis reveals some of the challenges associated with both device heterogeneity and the different use of smartwatches compared to smartphones. When using smartwatches to recognize whole body activities, the arm movements introduce additional variability giving rise to a significant degradation in HAR. In this analysis, we describe and evaluate several techniques which successfully address these challenges when using smartwatches and when training and testing with different devices and/or users. Highlights: Study of the differences between signals from smartphones and smartwatches. Increasing robustness for Human Activity Recognition (HAR). Feature extraction analysis: discrimination and robustness against degradation. Activity time modeling using Hidden Markov Models and Recurrent Neural Networks. Comparison of traditional and deep machine learning strategies.