The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

A tutorial on human activity recognition using body-worn inertial sensors

A Tutorial on Human Activity Recognition Using Body-Worn

In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

A Survey on Ambient-Assisted Living Tools for Older Adults

With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

/pdf/activity-identification-using-body-mounted-sensors-a-review-3yfkcniot4.pdf

Activity identification using body-mounted sensors — a review of classification techniques

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

/pdf/physical-human-activity-recognition-using-wearable-sensors-3o6vln9gc2.pdf

Physical Human Activity Recognition Using Wearable Sensors.

High-level and longer-term activity recognition has great potentials in areas such as medical diagnosis and human behavior modeling. So far however, activity recognition research has mostly focused on lowlevel and short-term activities. This paper therefore makes a first step towards recognition of high-level activities as they occur in daily life. For this we record a realistic 10h data set and analyze the performance of four different algorithms for the recognition of both low- and high-level activities. Here we focus on simple features and computationally efficient algorithms as this facilitates the embedding and deployment of the approach in real-world scenarios. While preliminary, the experimental results suggest that the recognition of high-level activities can be achieved with the same algorithms as the recognition of low-level activities.

/pdf/scalable-recognition-of-daily-activities-with-wearable-12dtx7ce0b.pdf

Scalable recognition of daily activities with wearable sensors

In this paper we propose a novel scheme for unsupervised detection of structure in activity data. Our method is based upon an algorithm that represents data in terms of multiple low-dimensional eigenspaces. We describe the algorithm and propose an extension that allows to handle multiple time scales. The validity of the approach is demonstrated on several data sets and using two types of acceleration features. Finally, we report on experiments that indicate that our approach can yield recognition rates comparable to other, supervised approaches.

http://www.mobvis.org/publications/huynh2006loca.pdf

Tâm Huỳnh

Papers

Scalable recognition of daily activities with wearable sensors

Unsupervised discovery of structure in activity data using multiple eigenspaces