13,896 research outputs found
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
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
Inertial sensor-based knee flexion/extension angle estimation
A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth’s magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed
It's the Human that Matters: Accurate User Orientation Estimation for Mobile Computing Applications
Ubiquity of Internet-connected and sensor-equipped portable devices sparked a
new set of mobile computing applications that leverage the proliferating
sensing capabilities of smart-phones. For many of these applications, accurate
estimation of the user heading, as compared to the phone heading, is of
paramount importance. This is of special importance for many crowd-sensing
applications, where the phone can be carried in arbitrary positions and
orientations relative to the user body. Current state-of-the-art focus mainly
on estimating the phone orientation, require the phone to be placed in a
particular position, require user intervention, and/or do not work accurately
indoors; which limits their ubiquitous usability in different applications. In
this paper we present Humaine, a novel system to reliably and accurately
estimate the user orientation relative to the Earth coordinate system.
Humaine requires no prior-configuration nor user intervention and works
accurately indoors and outdoors for arbitrary cell phone positions and
orientations relative to the user body. The system applies statistical analysis
techniques to the inertial sensors widely available on today's cell phones to
estimate both the phone and user orientation. Implementation of the system on
different Android devices with 170 experiments performed at different indoor
and outdoor testbeds shows that Humaine significantly outperforms the
state-of-the-art in diverse scenarios, achieving a median accuracy of
averaged over a wide variety of phone positions. This is
better than the-state-of-the-art. The accuracy is bounded by the error in the
inertial sensors readings and can be enhanced with more accurate sensors and
sensor fusion.Comment: Accepted for publication in the 11th International Conference on
Mobile and Ubiquitous Systems: Computing, Networking and Services
(Mobiquitous 2014
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