Journal of Ambient Intelligence and Smart Environments

Person identification is a process through which a person is recognized using some information about him-/herself. Usually this is performed by asking the user to perform some action, e.g., to apply a token (card), enter a PIN code, scan a finger, or something similar. This paper describes an approach for recognizing a person entering a room using door accelerations, i.e., no additional action is required. The approach analyzes the acceleration signal in time and frequency domain. For each domain two types of methods were developed: (i) feature-based – uses features to describe the acceleration and then uses classification method to identify the person; (ii) signal-based – uses the acceleration signal as input and finds the most similar ones in order to identify the person. The four methods were evaluated on a dataset of 1005 entrances recorded by 12 people. The results show that the time-domain methods achieve significantly higher accuracy compared to the frequency-domain methods, with signal-based method achieving 86% accuracy. Additionally, the four methods were combined and all 15 combinations were examined. The best performing combined method increased the accuracy to 90%. Additional experiments with varying the number of training instances, showed that around 10 to 20 training instances are enough to achieve reliable performance. The results confirm that it is possible to identify a person entering a room using only the door acceleration and that relatively high accuracy (over 95%) is expected for a limited group of dissimilar users, e.g. a typical family.

Recent human activity recognition (HAR) methods, based on on-body inertial sensors, have achieved increasing performance; however, this is at the expense of longer CPU calculations and greater energy consumption. Therefore, these complex models might not be suitable for real-time prediction in mobile systems, e.g., in elder-care support and long-term health-monitoring systems. Here, we present a new method called RapidHARe for real-time human activity recognition based on modeling the distribution of a raw data in a half-second context window using dynamic Bayesian networks. Our method does not employ any dynamic-programming-based algorithms, which are notoriously slow for inference, nor does it employ feature extraction or selection methods. In our comparative tests, we show that RapidHARe is an extremely fast predictor, one and a half times faster than artificial neural networks (ANNs) methods, and more than eight times faster than recurrent neural networks (RNNs) and hidden Markov models (HMMs). Moreover, in performance, RapidHare achieves an F1 score of 94.27% and accuracy of 98.94%, and when compared to ANN, RNN, HMM, it reduces the F1-score error rate by 45%, 65%, and 63% and the accuracy error rate by 41%, 55%, and 62%, respectively. Therefore, RapidHARe is suitable for real-time recognition in mobile devices.

This paper proposes a probabilistic, time efficient, data-driven method for human low and medium level activity recognition and indoor tracking. The obtained results can be applied to a probabilistic reasoner for high level activity recognition. The proposed method is tested on Opportunity, a dataset consisting of daily morning activities in a highly sensor-rich environment. The main objective of this research is to suggest and apply methods suitable for batch processing of big data. In this case, performance in terms of CPU time and efficiency in storage usage are the top priorities. We applied fast signal processing methods to compute proper features from different collections of sensor signals. The relevant collections of features are selected and fed into a classifier to obtain results in the form of probability for each instance belonging to available classes. Additionally, the most probable locations of each subject in the room are calculated by processing noisy data from location tags on the subjects’ body. Afterwards, the proposed probabilistic data smoothing method is applied to further increase accuracy. To evaluate the methods, the most probable recognitions are benchmarked against the results of the Opportunity Challenge competitions as well as provided results by the Opportunity group. We also implemented a couple of well-known methods on the current dataset and compared them with ours. Moreover, the performance of different sensors assemblies is investigated. Our proposed method could obtain very close results in terms of accuracy while it is more optimal in terms of number of features and required time.

Aquaculture, in addition to agriculture, is one of the most sought after occupations in the coastal regions of India. The livelihood of thousands of aquaculture farmers depends on the output generated through aquaculture and thus this is one of the major factors influencing the socio-economic status of the country. However, the current practices adopted by these farmers in developing countries are very traditional and need to improve in order to get higher yield and production. This paper presents an intensive review of the design of WSN (Wireless Sensor Network) in aquaculture. Indian scenario of aquaculture is represented through surveys and case studies that were conducted in Bhimavaram, a city in western Godavari region of Andhra Pradesh. A system design based on wireless sensor network is proposed which enables remote monitoring of the aquaculture farms and sending alerts to the farmers when any deteriorating deviation in tank water quality is detected.