Human activity recognition from object interaction in domestic scenarios

This paper presents a real time approach to the recognition of human activity based on the interaction between people and objects in domestic settings, specifically in a kitchen. Regarding the procedure, it is based on capturing partial images where the activity takes place using a colour camera, and processing the images to recognize the present objects and their location. For object description and recognition, a histogram on rg chromaticity space has been selected. The interaction with the objects is classified into four types of possible actions; (unchanged, add, remove or move). Activities are defined as recipes, where objects play the role of ingredients, tools or substitutes. Sensed objects and actions are then used to analyze in real time the probability of the human activity performed at a particular moment in a continuous activity sequence.Peer ReviewedPostprint (author's final draft

Location Based Indoor and Outdoor Lightweight Activity Recognition System

In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure

Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.Comment: submitted to the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON

Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning

Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human-Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate

From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern and the subjects do not have to carry a device on them. Recently, it has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments show that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. We are also the first to use complex-valued CSI to improve the performance in the environment with RFI

Human Activity Recognition System Including Smartphone Position

AbstractThe data gathered by acceleration sensors in smartphones gives different results depending on the location of the smartphone. In this paper, a human activity recognition system was proposed, including the smartphone's position. This system can recognize not only the activity of a person, but also the location of the smartphone. HOG (Histograms of Oriented Gradients) were used to extract features of the acceleration data, because the waveform of the acceleration data is very complex. Then, a strong classifier was obtained using a learning algorithm of Real AdaBoost based on the position of possession smartphone and acceleration sensor data. It also improves the recognition rate by analyzing the acceleration data. The effectiveness of the activity recognition system was shown by the experiment

A Novel Approach to Complex Human Activity Recognition

Human activity recognition is a technology that offers automatic recognition of what a person is doing with respect to body motion and function. The main goal is to recognize a person\u27s activity using different technologies such as cameras, motion sensors, location sensors, and time. Human activity recognition is important in many areas such as pervasive computing, artificial intelligence, human-computer interaction, health care, health outcomes, rehabilitation engineering, occupational science, and social sciences. There are numerous ubiquitous and pervasive computing systems where users\u27 activities play an important role. The human activity carries a lot of information about the context and helps systems to achieve context-awareness. In the rehabilitation area, it helps with functional diagnosis and assessing health outcomes. Human activity recognition is an important indicator of participation, quality of life and lifestyle. There are two classes of human activities based on body motion and function. The first class, simple human activity, involves human body motion and posture, such as walking, running, and sitting. The second class, complex human activity, includes function along with simple human activity, such as cooking, reading, and watching TV. Human activity recognition is an interdisciplinary research area that has been active for more than a decade. Substantial research has been conducted to recognize human activities, but, there are many major issues still need to be addressed. Addressing these issues would provide a significant improvement in different aspects of the applications of the human activity recognition in different areas. There has been considerable research conducted on simple human activity recognition, whereas, a little research has been carried out on complex human activity recognition. However, there are many key aspects (recognition accuracy, computational cost, energy consumption, mobility) that need to be addressed in both areas to improve their viability. This dissertation aims to address the key aspects in both areas of human activity recognition and eventually focuses on recognition of complex activity. It also addresses indoor and outdoor localization, an important parameter along with time in complex activity recognition. This work studies accelerometer sensor data to recognize simple human activity and time, location and simple activity to recognize complex activity

Rule-based Location and Activity Recognition Based on Environmental Sensors

Tervishoiuteenuste kulude vähendamiseks on kasulik laiendada tervishoiusüsteemi ka kodukeskkonnale. Üks võimalus selleks on luua patsiendi kodusse sensorisüsteem, mille abil saab tervisevaldkonna spetsialist vajalikku infot patsiendi abistamiseks või raviks. Projektis SPHERE on sellesuunaliseks uurimistööks loodud eksperimentaalne maja, mis on sisustatud mitmesuguste sensoritega. Bakalaureusetöö eesmärgiks oli luua asukoha ja tegevuse tuvastamise jaoks automaatne reeglitepõhine süsteem SPHERE projekti maja jaoks. Töö käigus valmis süsteem, mis tuvastab edukalt peaaegu kõik ruumides viibimised, eksides valdavalt alla kahe sekundi. Tegevuste tuvastamiseks ei anna ruumisensorid palju võimalusi, millest tingitult tuvastatakse üksikuid tegevusi, mille ajalised eksimused jäävad alla kümne sekundi.It is useful to extend the health care system to home environment in order to reduce the healthcare costs. One solution for that would be to create a sensor system that provides helthcare professionals with necessary information for assisting patient's treatment. SPHERE project has developed an experimental building for mentioned purposes that is equipped with sensors. The aim of this bachelor’s thesis is to develop an automatic system that is capable of recognising human locations and activity using rule-based approach. The developed system is able to detect successfully all the rooms, where the patient has been with error less than two seconds most of the time. Room sensors don't give many opportunities for recognising activities, hence the system is capable of recognising a few of them while being mistaken less than ten seconds