Benchmarking classification techniques using the Opportunity human activity dataset

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activity recognition. Typically, each research group tests and reports the performance of their algorithms on their own datasets using experimental setups specially conceived for that specific purpose. In this work, we introduce a versatile human activity dataset conceived to fill that void. We illustrate its use by presenting comparative results of different classification techniques, and discuss about several metrics that can be used to assess their performance. Being an initial benchmarking, we expect that the possibility to replicate and outperform the presented results will contribute to further advances in state-of-the-art methods

Evaluating Performance in Continuous Context Recognition Using Event-Driven Error Characterisation

Evaluating the performance of a continuous activity recognition system can be a challenging problem. To-date there is no widely accepted standard for dealing with this, and in general methods and measures are adapted from related fields such as speech and vision. Much of the problem stems from the often imprecise and ambiguous nature of the real-world events that an activity recognition system has to deal with. A recognised event might have variable duration, or be shifted in time from the corresponding real-world event. Equally it might be broken up into smaller pieces, or joined together to form larger events. Most evaluation attempts tend to smooth over these issues, using âÂ�Â�fuzzyâÂ�Â�boundaries, or some other parameter based error decision, so as to make possible the use of standard performance measures (such as insertions and deletions.) However, we argue that reducing the various facets of a activity system into limited error categories - that were originally intended for different problem domains - can be overly restrictive. In this paper we attempt to identify and characterise the errors typical to continuous activity recognition, and develop a method for quantifying them in an unambiguous manner

Using machine learning for real-time activity recognition and estimation of energy expenditure

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 481-493).Obesity is now considered a global epidemic and is predicted to become the number one preventive health threat in the industrialized world. Presently, over 60% of the U.S. adult population is overweight and 30% is obese. This is of concern because obesity is linked to leading causes of death, such as heart and pulmonary diseases, stroke, and type 2 diabetes. The dramatic rise in obesity rates is attributed to an environment that provides easy access to high caloric food and drink and promotes low levels of physical activity. Unfortunately, many people have a poor understanding of their own daily energy (im)balance: the number of calories they consume from food compared with what they expend through physical activity. Accelerometers offer promise as an objective measure of physical activity. In prior work they have been used to estimate energy expenditure and activity type. This work further demonstrates how wireless accelerometers can be used for real-time automatic recognition of physical activity type, intensity, and duration and estimation of energy expenditure. The parameters of the algorithms such as type of classifier/regressor, feature set, window length, signal preprocessing, sensor set utilized and their placement on the human body are selected by performing a set of incremental experiments designed to identify sets of parameters that may balance system usability with robust, real-time performance in low processing power devices such as mobile phones. The algorithms implemented are evaluated using a dataset of examples of 52 activities collected from 20 participants at a gymnasium and a residential home. The algorithms presented here may ultimately allow for the development of mobile phone-based just-in-time interventions to increase self-awareness of physical activity patterns and increases in physical activity levels in real-time during free-living that scale to large populations.(cont.) KEYWORDS: Activity recognition, context awareness, energy expenditure, physical activity, wearable sensors, obesity, mobile phone, pattern recognition, machine learning, ubiquitous, pervasive, just-in-time.by Emmanuel Munguia Tapia.Ph.D