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

Frequency based Classification of Activities using Accelerometer Data

This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.Comment: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 200

Sensor-Based Activity Recognition and Performance Assessment in Climbing: A Review

In the past decades, a number of technological developments made it possible to continuously collect various types of sport activity data in an unobtrusive way. Machine learning and analytical methods have been applied to flows of sensor data to predict the conducted sport activity as well as to calculate key performance indicators. In that scenario, researchers started to be interested in leveraging pervasive information technologies for sport climbing, thus allowing, in day-to-day climbing practice, the realization of systems for automatic assessment of a climber’s performance, detection of injury risk factors, and virtual coaching. This article surveys recent research works on the recognition of climbing activities and the evaluation of climbing performance indicators, where data have been acquired with accelerometers, cameras, force sensors, and other types of sensors. We describe the main types of sensors and equipment adopted for data acquisition, the techniques used to extract relevant features from sensor data, and the methods that have been proposed to identify the activities performed by a climber and to calculate key performance indicators. We also present a classification taxonomy of climbing activities and of climbing performance indicators, with the aim to unify the existing work and facilitate the comparison of methods. Moreover, open problems that call for new approaches and solutions are here discussed. We conclude that there is considerable scope for further work, particularly in the application of recognition techniques to problems involving various climbing activities. We hope that this survey will assist in the translation of research effort into intelligent environments that climbers will benefit from

Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents.

Wearable acceleration sensors are increasingly used for the assessment of free-living physical activity. Acceleration sensor calibration is a potential source of error. This study aims to describe and evaluate an autocalibration method to minimize calibration error using segments within the free-living records (no extra experiments needed). The autocalibration method entailed the extraction of nonmovement periods in the data, for which the measured vector magnitude should ideally be the gravitational acceleration (1 g); this property was used to derive calibration correction factors using an iterative closest-point fitting process. The reduction in calibration error was evaluated in data from four cohorts: UK (n = 921), Kuwait (n = 120), Cameroon (n = 311), and Brazil (n = 200). Our method significantly reduced calibration error in all cohorts (P 0.05). Temperature correction coefficients were highest for the z-axis, e.g., 19.6-mg offset per 5°C. Further, application of the autocalibration method had a significant impact on typical metrics used for describing human physical activity, e.g., in Brazil average wrist acceleration was 0.2 to 51% lower than uncalibrated values depending on metric selection (P < 0.01). The autocalibration method as presented helps reduce the calibration error in wearable acceleration sensor data and improves comparability of physical activity measures across study locations. Temperature ultization seems essential when temperature deviates substantially from the average temperature in the record but not for multiday summary measures.This is the final version of the article. It first appeared from the American Physiological Society via http://dx.doi.org/10.1152/japplphysiol.00421.201