Search Results (8)

Artificial intelligence has increasingly taken over various aspects of daily life, resulting in the proliferation of smart devices and the development of smart living and working environments. One significant domain within this technological advancement is human activity recognition, which includes a broad spectrum of applications such as patient monitoring and supervision of children’s activities. In this research, we endeavor to design a human activity recognition system that effectively analyzes multi-user data through a machine learning framework centered on graphical plot images. The proposed methodology uses a PIR sensor-based system. This system uses a two-stage process; the first one involves generating new image datasets as density map images and graphical representations based on the Kyoto CASAS multi-user dataset. In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. To generate the new datasets, we only used data from ambient sensors, which were organized in windows. We tested many types of window dimensions and extra features such as temporal aspect and the limitation of two activities in one window. The neural network was optimized by increasing the deconvolutional layers and adding the AdamW optimizer. The results demonstrate the viability of this method, evidencing an accuracy rate of 83% for multi-user activity and an accuracy rate of 99% for single-user activity. This study successfully achieved its objective of identifying an efficient activity recognition methodology and a data image representation. Furthermore, future enhancements are anticipated by integrating data sourced from PIR sensors, with information gathered from user-personal devices such as smartphones. This approach is also applicable to real-time recognition systems. Full article

Human activity recognition (HAR) in real-world settings has gained significance due to the growth of Internet of Things (IoT) devices such as smartphones and smartwatches. Nonetheless, limitations such as fluctuating environmental conditions and intricate behavioral patterns have impacted the accuracy of the current procedures. This research introduces an innovative methodology employing a modified deep residual network, called 1D-ResNeXt, for IoT-enabled HAR in uncontrolled environments. We developed a comprehensive network that utilizes feature fusion and a multi-kernel block approach. The residual connections and the split–transform–merge technique mitigate the accuracy degradation and reduce the parameter number. We assessed our suggested model on three available datasets, mHealth, MotionSense, and Wild-SHARD, utilizing accuracy metrics, cross-entropy loss, and F1 score. The findings indicated substantial enhancements in proficiency in recognition, attaining 99.97% on mHealth, 98.77% on MotionSense, and 97.59% on Wild-SHARD, surpassing contemporary methodologies. Significantly, our model attained these outcomes with considerably fewer parameters (24,130–26,118) than other models, several of which exceeded 700,000 parameters. The 1D-ResNeXt model demonstrated outstanding effectiveness under various ambient circumstances, tackling a significant obstacle in practical HAR applications. The findings indicate that our modified deep residual network presents a viable approach for improving the dependability and usability of IoT-based HAR systems in dynamic, uncontrolled situations while preserving the computational effectiveness essential for IoT devices. The results significantly impact multiple sectors, including healthcare surveillance, intelligent residences, and customized assistive devices. Full article

As the world’s population is aging, and since access to ambient sensors has become easier over the past years, activity recognition in smart home installations has gained increased scientific interest. The majority of published papers in the literature focus on single-resident activity recognition. While this is an important area, especially when focusing on elderly people living alone, multi-resident activity recognition has potentially more applications in smart homes. Activity recognition for multiple residents acting concurrently can be treated as a multilabel classification problem (MLC). In this study, an experimental comparison between different MLC algorithms is attempted. Three different techniques were implemented: RAkEL${}_d$, classifier chains, and binary relevance. These methods are evaluated using the ARAS and CASAS public datasets. Results obtained from experiments have shown that using MLC can recognize activities performed by multiple people with high accuracy. While RAkEL${}_d$ had the best performance, the rest of the methods had on-par results. Full article

Portraying functional urban areas provides useful insights for understanding complex urban systems and formulating rational urban plans. Mobile phone user trajectory data are often used to infer the individual activity patterns of people and for functional area identification, but they are difficult to obtain because of personal privacy issues and have the drawback of a sparse spatial and temporal distribution. Deep learning models have been widely utilized in functional area recognition but are limited by the difficulty of acquiring training samples with large data volumes. This paper aims to achieve a fast and automatic identification of large-scale urban functional areas without prior knowledge. This paper uses Nanjing city as a test area, and a self-organizing map (SOM) neural network model based on an improved dynamic time warping (Ndim-DTW) distance is used to automatically identify the function of each building using mobile phone aggregated data containing work and residence attributes. The results show that the recognition accuracy reaches 88.7%, which is 12.4% higher than that of the K-medoids method based on the DTW distance using a single attribute and 7.8% higher than that of the K-medoids method based on the Ndim-DTW distance with multiple attributes, confirming the effectiveness of the multi-attribute mobile phone aggregated data and the SOM model based on the Ndim-DTW distance. Furthermore, at the traffic analysis zone (TAZ) level, this paper detects that Nanjing has seven functional area hotspots with a high degree of mixing. The results can provide a data basis for urban studies on, for example, the urban spatial structure, the separation of occupations and residences, and environmental suitability evaluation. Full article

Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments. Full article

The economic development of a territory is strongly correlated to its level of infrastructure (railway, roads, etc.); the complexity of this type of works requires careful planning and design that cannot be separated from the assessment of the impacts generated on citizenship affected by the new infrastructures. This study deals with the instrument defined by the Liguria Region for the implementation of infrastructures through the instruments called “Programmi Regionali di Intervento Strategico—P.R.I.S.” (Regional Strategic Intervention Programs) established by the Regional Law n. 39/2007. The aim of the P.R.I.S. is to guarantee the social protection of citizens that reside (as owners or tenants) or carry out economic activities in real estate units incompatible with the construction of the infrastructure, according to the main Italian law (Presidential Decree n. 327/2001) about the expropriation of private real estate for the construction of public works. In particular, the construction of a new link of the A7-A10-A12 motorway sections near the city of Genoa (called “Gronda”) is considered. The new infrastructure involves the expropriation of about 100 residential units and the relocation of about 50 production activities; the related P.R.I.S. defines the conditions that allow social cohesion through the recognition of indemnities for the expropriation of the real estate properties and the compensation of other expenses that the residents have to pay for their relocation. The valuation of the indemnities is developed through a multi-parameter model applicable for the estimation of real estate units (residential and productive) at a large-scale (mass appraisal); it is derived from the Market Comparison Approach and considers the most meaningful real estate characteristics. The aim is to develop a mass appraisal estimation model applicable in an easy way on real estate units with different destinations use. The model can be applied for the estimation of ordinary and special indemnities to be recognized for owners and tenants affected by the expropriation of their real estate units for the construction of public projects. Full article

With the development of population aging, the recognition of elderly activity in smart homes has received increasing attention. In recent years, single-resident activity recognition based on smart homes has made great progress. However, few researchers have focused on multi-resident activity recognition. In this paper, we propose a method to recognize two-resident activities based on time clustering. First, to use a de-noising method to extract the feature of the dataset. Second, to cluster the dataset based on the begin time and end time. Finally, to complete activity recognition using a similarity matching method. To test the performance of the method, we used two two-resident datasets provided by Center for Advanced Studies in Adaptive Systems (CASAS). We evaluated our method by comparing it with some common classifiers. The results show that our method has certain improvements in the accuracy, recall, precision, and F-Measure. At the end of the paper, we explain the parameter selection and summarize our method. Full article

To recognize individual activities in multi-resident environments with pervasive sensors, some researchers have pointed out that finding data associations can contribute to activity recognition and previous methods either need or infer data association when recognizing new multi-resident activities based on new observations from sensors. However, it is often difficult to find out data associations, and available approaches to multi-resident activity recognition degrade when the data association is not given or induced with low accuracy. This paper exploits some simple knowledge of multi-resident activities through defining Combined label and the state set, and proposes a two-stage activity recognition method for multi-resident activity recognition. We define Combined label states at the model building phase with the help of data association, and learn Combined label states at the new activity recognition phase without the help of data association. Our two stages method is embodied in the new activity recognition phase, where we figure out multi-resident activities in the second stage after learning Combined label states at first stage. The experiments using the multi-resident CASAS data demonstrate that our method can increase the recognition accuracy by approximately 10%. Full article