Green multimedia: informing people of their carbon footprint through two simple sensors

In this work we discuss a new, but highly relevant, topic to the multimedia community; systems to inform individuals of their carbon footprint, which could ultimately effect change in community carbon footprint-related activities. The reduction of carbon emissions is now an important policy driver of many governments, and one of the major areas of focus is in reducing the energy demand from the consumers i.e. all of us individually. In terms of CO2 generated from energy consumption, there are three predominant factors, namely electricity usage, thermal related costs, and transport usage. Standard home electricity and heating sensors can be used to measure the former two aspects, and in this paper we evaluate a novel technique to estimate an individual's transport-related carbon emissions through the use of a simple wearable accelerometer. We investigate how providing this novel estimation of transport-related carbon emissions through an interactive web site and mobile phone app engages a set of users in becoming more aware of their carbon emissions. Our evaluations involve a group of 6 users collecting 25 million accelerometer readings and 12.5 million power readings vs. a control group of 16 users collecting 29.7 million power readings

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

Comparing the Performance of Machine Learning Algorithms for Human Activities Recognition using WISDM Dataset

Human activity recognition is an important area of machine learning research as it has much utilization in different areas such as sports training, security, entertainment, ambient-assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Mobile phones are used to be more than luxury products, it has become a kind of urgent need for a fast-moving world with rapid development. Nowadays mobile phone is well equipped with advanced processor, more memory, powerful battery and built-in sensors. This provides an opportunity to open up new areas of data mining for activity recognition of human’s daily living. In this paper, we tested experiment using Tree based Classifiers (Decision Tree, J48, JRIP, and Random Forest) and Rule based algorithms Classifiers (Naive Bayes and AD1) to classify six activities of daily life by using Weka tool. According to the tested results Random Forest classifier is more accurate than other classifiers

THE USE OF THE WEARABLE SENSORY DEVICES FOR METABOLIC RATE ESTIMATION

The human metabolic rate has been widely noted as the least accurately assessed parameter in the research of thermal comfort. Since it is difficult to measure, it is often reduced on simple diary methods which do not take into account the influence of age, gender, and daily dynamics of a building occupant. In this study wearable sensory device were used to track user activities inside the building to estimate the metabolic rate. Eight participants were chosen for the 7-days monitoring, four times during the year. The study aimed to examine whether the user age, gender, and season of the year influence the change in user metabolic rate to improve the thermal environment of the HVAC system building. The results showed the difference between groups of building occupants that could serve as a reference to assess personal thermal comfort in future research

Ubiquitous Health Management System with Watch-Type Monitoring Device for Dementia Patients

For patients who have a senile mental disorder such as dementia, the quantity of exercise and amount of sunlight are an important clue for doses and treatment. Therefore, monitoring daily health information is necessary for patients’ safety and health. A portable and wearable sensor device and server configuration for monitoring data are needed to provide these services for patients. A watch-type device (smart watch) that patients wear and a server system are developed in this paper. The smart watch developed includes a GPS, accelerometer, and illumination sensor, and can obtain real time health information by measuring the position of patients, quantity of exercise, and amount of sunlight. The server system includes the sensor data analysis algorithm and web server used by the doctor and protector to monitor the sensor data acquired from the smart watch. The proposed data analysis algorithm acquires the exercise information and detects the step count in patients’ motion acquired from the acceleration sensor and verifies the three cases of fast pace, slow pace, and walking pace, showing 96% of the experimental results. If developed and the u-Healthcare System for dementia patients is applied, higher quality medical services can be provided to patients

Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities within the Home

One of the key objectives of an ambient assisted living environment is to enable elderly people to lead a healthy and independent life. These assisted environments have the capability to capture and infer activities performed by individuals, which can be useful for providing assistance and tracking functional decline among the elderly community. This paper presents an activity recognition engine based on a hierarchal structure, which allows modelling, representation and recognition of ADLs, their associated tasks, objects, relationships and dependencies. The structure of this contextual information plays a vital role in conducting accurate ADL recognition. The recognition performance of the inference engine has been validated with a series of experiments based on object usage data collected within the home environment