Smartwatch Sensors Research Articles

GroupSense

Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense using smartphone sensors, smartwatch sensors, and embedded sensors in things, where we have a protocol for these different devices to exchange information required for GAR. A range of continuous group activities (from simple to fairly complex) illustrates our approach and demonstrates the feasibility of our model and richness of the proposed specialization. We then conclude with lessons learned for GAR and future work.

Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm

Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.

IoT Middleware: A Survey on Issues and Enabling technologies

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

Zone-aware service system with nomadic resources for cost-effective pervasive infrastructure

The context-aware service system (CASS) for smart home services has received considerable attention due to advances made in context-awareness technologies. In recent years, various context-aware consumer electronics for intelligent and personalized user-centric services have been introduced. However, the conventional CASS has architectural limitations, including low availability, low interoperability, and low scalability. For example, it is difficult to modify or update the context-aware service (CAS) infrastructure once it has been built. Therefore, this paper proposes a zone-aware service system (ZASS) for cost-effective pervasive infrastructure. The main component of the ZASS is a zone-aware service agent (ZASA) which utilizes the user’s nomadic resource as a sensor or gateway. The term ‘user’s nomadic resource’ refers to the user’s hand-held device, which has a sensing or networking capability, such as a smartphone, smart watch, and mobile sensor, etc. As the utilization of the user’s nomadic resource, the ZASS can be cost-effectively implemented compared with the conventional CASS. The performance of the proposed system is discussed via a simple smart home service scenario. The experimental result showed that the proposed system can reduce the power consumption of an air conditioner by 20.7%.