Multi-sensor Activity Recognition Research Articles

Sensor positioning for a human activity recognition system using a double layer classifier.

This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor's optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.

Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

Personal wearables such as smartphones or smartwatches are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on the creation of a self-adaptive activity recognition system based on IMU that includes new sensors during runtime. Starting with a classifier based on GMM, the density model is adapted to new sensor data fully autonomously by issuing the marginalization property of normal distributions. To create a classifier from that, label inference is done, either based on the initial classifier or based on the training data. For evaluation, we used more than 10 h of annotated activity data from the publicly available PAMAP2 benchmark dataset. Using the data, we showed the feasibility of our approach and performed 9720 experiments, to get resilient numbers. One approach performed reasonably well, leading to a system improvement on average, with an increase in the F-score of 0.0053, while the other one shows clear drawbacks due to a high loss of information during label inference. Furthermore, a comparison with state of the art techniques shows the necessity for further experiments in this area.

A wavelet tensor fuzzy clustering scheme for multi-sensor human activity recognition

With the increasing number of wearable sensors and mobile devices, human activity recognition (HAR) based on multiple sensors has attracted more and more attention in recent years. On account of the diversity of human actions, the analysis of multivariate signals of activities is still a challenging task. Clustering is an unsupervised classification technique which can directly work on unlabeled data and automatically identify unknown activities. Therefore, a new wavelet tensor fuzzy clustering scheme (WTFCS) for multi-sensor activity recognition is proposed in this paper. Firstly, feature tensors of multiple activity signals are constructed using the discrete wavelet packet transform (DWPT). Then Multilinear Principal Component Analysis (MPCA) is utilized to reduce the dimensionality of feature tensors so as to keep the inherent data structure. On the basis of the principal feature initialization and the tensor fuzzy membership, a new fuzzy clustering (PTFC) is developed to identify different activity feature tensor groups. Finally, the open HAR dataset (DSAD) is used to verify the efficiency of the WTFCS. Clustering results of seventeen activities of eight subjects show that potential useful features of human activities can be captured through combining DWPT-based feature extraction with MPCA-based dimensionality reduction. The PTFC is capable of discriminating various human activities effectively. Its correctness rate of activity recognition is higher than those of fuzzy c-means clustering and the fuzzy clustering based on the tensor distance.

The Performance Analysis of Smartphone-Based Pedestrian Dead Reckoning and Wireless Locating Technology for Indoor Navigation Application

Recent developments in smartphone technology have increased user demand for indoors applications. The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) are the two advanced systems for navigation technology. However, it is still difficult for GNSS to provide an accurate and practical navigation solution, especially in environments with little or no signal availability. These failings should be easy to overcome; because of their portability and multiple embedded hardware sensors, smartphones seem well positioned to make pedestrian navigation easy and convenient in any environment. Pedestrian Dead Reckoning (PDR) is one of the most commonly used technologies used for pedestrian navigation, but it is not always accurate because its positioning errors tend to accumulate over time. Therefore, this research introduces a new tool to overcome this failing; a Bluetooth Low-Energy (BLE) beacon can maintain and improve the accuracy of PDR. Moreover, a BLE beacon can be initialized from any user position in an indoor environment. The random and unpredictable positions of pedestrians inevitably result in the degradation of navigation guidance systems’ data. To rectify this problem, we have implemented activity recognition technology to notify the proposed system so as to provide a more accurate heading estimate. This study proposes a Personal Navigation System (PNS) based on this technology; it can estimate navigation solutions in real time and combines the advantages of PDR and Bluetooth positioning technology. A series of experiments were conducted to evaluate the accuracy of the system and the efficacy of our proposed algorithms. Preliminary results show the average relative precision of PDR to be about 2.5%, when using a mobile hand-held device. The error of initial position from 2-D beacon positioning is less than two meters. The proposed system works well without post-processing, and the multi-sensor activity recognition system can determine the placement of the device when it is being carried or used by someone with close to 100% accuracy.

Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition

In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary (MRMC) based on neural networks. Unlike other feature selection algorithms that are based on relevance and redundancy measurements, the idea of how a feature complements to the already selected features is utilized. The proposed algorithm is evaluated on two well-defined problems and five real world data sets. The data sets cover different types of data i.e. real, integer and category and sizes i.e. small to large set of features. The experimental results show that the MRMC can select a smaller number of features while achieving good results. The proposed algorithm can be applied to any type of data, and demonstrate great potential for the data set with a large number of features.

A practical multi-sensor activity recognition system for home-based care

To cope with the increasing number of aging population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care.

Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.

Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.