IoT based activity recognition among smart home residents

Activity recognition in smart home environment is actively pursued for accessing changes in physical and behavioral profiles of home dwellers. Various activity recognition solutions have been previously proposed to implement a system with wearable sensors and smartphones. Although such solutions are widely integrated, the availability of the activity data in seamless way still poses interesting research challenges. Internet of Things (IoT) is seen as new paradigm, revolutionizing consumer electronics by extending Internet connectivity to many physical objects associated with consumer's daily life. In this paper, an Internet of Things (IoT) based activity recognition framework is proposed for activity monitoring within consumer home network. Our proposed Elgar framework handles management of activity recognition via IoT services in an IoT environment with multiple devices. The performance evaluation done pointed that the proposed system can robustly identify the activities using IoT in smart home environment with high accuracy. Hence, this system could be reliably deployed into a consumer product for the usage of home dwellers

Activity-aware Stress Sensory System

Continuous stress monitoring may able to help analyzing and enhance the awareness of an individual on their stress patterns and provide more reliable data information for physicians in interventions. In the past years research, studies on mental stress sensory system were limited inside laboratory environment. However, excluding the effects of physical activity can be impractical while developing a wearable stress sensory system for daily use. In this project, effects of external factors from environment on Galvanic Skin Response (GSR) measurements and integration of several stress sensory system were studied. Electrocardiogram (ECG), GSR, and Activity Recognition System (ARS) were studied under different physical activities: sitting, standing, lying and walking. It is showed from the studies that an overall accuracy of 94.7% in ARS is achieved by using two sensor node system (at thigh and ankle each) which an improvement of 27.3% from using single sensor node system. It is further demonstrated that ARS could help improve in accuracy of wearable stress sensory system

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

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

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Designing Auditory Feedback from Wearable Weightlifting Devices

While wearable devices for fitness have gained broad popularity, most are focused on tracking general activity types rather than correcting exercise forms, which is extremely important for weightlifters. We interviewed 7 frequent gym-goers about their opinions and expectations for feedback from wearable devices for weightlifting. We describe their desired feedback, and how their expectations and concerns could be balanced in future wearable fitness technologies

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio