Movement analysis with inertial sensors: identification of noise covariance matrices to improve body segment orientation using Kalman filtering

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

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The Use of Wearable Sensors in Human Movement Analysis in Non-Swimming Aquatic Activities: A Systematic Review

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16245067 ◽

2019 ◽

Vol 16 (24) ◽

pp. 5067

Author(s):

Daniel A. Marinho ◽

Henrique P. Neiva ◽

Jorge E. Morais

Keyword(s):

Systematic Review ◽

Aquatic Environment ◽

Web Of Science ◽

Inertial Sensors ◽

State Of The Art ◽

Wearable Sensors ◽

Movement Analysis ◽

Human Movement ◽

Dry Land ◽

Swimming Kinematics

The use of smart technology, specifically inertial sensors (accelerometers, gyroscopes, and magnetometers), to analyze swimming kinematics is being reported in the literature. However, little is known about the usage/application of such sensors in other human aquatic exercises. As the sensors are getting smaller, less expensive, and simple to deal with (regarding data acquisition), one might consider that its application to a broader range of exercises should be a reality. The aim of this systematic review was to update the state of the art about the framework related to the use of sensors assessing human movement in an aquatic environment, besides swimming. The following databases were used: IEEE Xplore, Pubmed, Science Direct, Scopus, and Web of Science. Five articles published in indexed journals, aiming to assess human exercises/movements in the aquatic environment were reviewed. The data from the five articles was categorized and summarized based on the aim, purpose, participants, sensor’s specifications, body area and variables analyzed, and data analysis and statistics. The analyzed studies aimed to compare the movement/exercise kinematics between environments (i.e., dry land versus aquatic), and in some cases compared healthy to pathological participants. The use of sensors in a rehabilitation/hydrotherapy perspective may provide major advantages for therapists.

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The Application of Adaptive Kalman Filter in Traffic Flow Forecasting

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.680.495 ◽

2013 ◽

Vol 680 ◽

pp. 495-500 ◽

Cited By ~ 4

Author(s):

Jun Wei Gao ◽

Zi Wen Leng ◽

Bin Zhang ◽

Xin Liu ◽

Guo Qiang Cai

Keyword(s):

Kalman Filter ◽

Traffic Flow ◽

Kalman Filtering ◽

Intelligent Transportation System ◽

Urban Traffic ◽

Forecasting Model ◽

Adaptive Kalman Filter ◽

Forecasting Accuracy ◽

Traffic Flow Forecasting ◽

Noise Covariance

The urban traffic usually has the characteristics of time-variation and nonlinearity, real-time and accurate traffic flow forecasting has become an important component of the Intelligent Transportation System (ITS). The paper gives a brief introduction of the basic theory of Kalman filter, and establishes the traffic flow forecasting model on the basis of the adaptive Kalman filter, while the traditional Kalman filtering model has the shortcomings of lower forecasting accuracy and easily running into filtering divergence. The Sage&Husa adaptive filtering algorithm will appropriately estimate and correct the unknown or uncertain noise covariance, so as to improve the dynamic characteristics of the model. The simulation results demonstrate that the adaptive Kalman filtering forecasting model has stronger tracking capability and higher forecasting precision, which is applicable to the traffic flow forecasting.

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