Monitoring of a person's daily activities can provide valuable information for health care and prevention and can be an important supportive application in the field of ambient assisted living (AAL). The goals of this study are the classification of postures and activities using knowledge-based methods as well as the evaluation of the performance of these methods. The acceleration data are gained by a single tri-axial accelerometer, which is mounted on a specific position on the test subject. A data set for training and testing was gained by collecting data from subjects, who performed varying postures and activities. For these purposes, three different knowledge-based (decision tree and neural network) classification methods and a hybrid classifier were implemented, tested and evaluated. The results of the tests illustrated that the hybrid classifier performed best with an overall accuracy of 98.99%. The advantages of knowledge-based methods are the exchangeable knowle dge base, which can be developed for different types of sensor positions and the state of health of the subject
Early in the 20th century it could be observed that the finger lengths of second (2D) and fourth digit (4D) represents a sexually dimorphic feature, whereby in general the second digit is longer than the fourth digit for men and vice versa for women. In the early 1980s first studies could show a correlation between the 2D:4D ratio and the concentration of androgens one is exposed to during a short window within the prenatal development phase. Therefore, the 2D:4D digit ratio can serve as an additional marker for certain physiological and psychological traits like fertility, assertiveness, aggressiveness and alcohol addiction. Manual measurements are the established method to retrieve the finger lengths e.g. by using a ruler on printed scans of hands. For higher reliability these extremely time-consuming procedures have to be done multiple times. In this contribution two automated procedures are proposed to reduce the time for measurements whilst maintaining the accuracy. A deviation of maximal 2% from the manual measurement could be achieved for more than 75% of the 22 participants
The electrocardiogram (ECG) is one of the most reliable information sources for assessing cardiovascular health and training success. Since the early 1990s, the heart rate variability (HRV), namely the variation from beat to beat, has become the focus of investigations as it provides insight into the complex interplay of body circulation and the influence of the autonomic nervous system on heartbeats. However, HRV parameters during physical activity are poorly understood, mostly due to the challenging signal processing in the presence of motion artefacts. To derive HRV parameters in time (heart rate (HR)) and frequency domains (high frequency (HF), low frequency (LF)), it is crucial to reliably detect the exact position of the R-peaks. We introduce a full algorithm chain where a sophisticated filtering technique is combined with an enhanced R-peak detection that can cope with motion artefacts in ECG data originating from physical activity
People can greatly benefit from mobile technologies that continuously monitor their vital signs, in medicine as well as in home environments and sports. In order to meet the requirements of mobile systems the algorithms have to be robust, reliable, take the limited resources into account and overcome the drawback of motion artefacts. This paper presents the evaluation of an algorithm for QRS detection based on ECG signals from a sensorized garment. The system saves the ECG data, measured via two textile electrodes sewed into the shirt, on a microSD card using the EDF+-format. The raw data is processed on a desktop PC using a modified state-of-the-art algorithm. QRS complexes and R-peaks of electrocardiographic signals are detected using the technique of zero crossings. Hereby, main focus has to be placed on the proper specification of the band pass filter, which is the basis for high accuracy. For the evaluation a well-defined test protocol has been specified. Six activities respectively postures were defined: Sitting, standing, walking, running, cycling and rowing. Each activity was performed by 10 test persons for a fixed time interval. Various parameters, where the temporal location of the R-peak is of importance, can be derived from the recorded ECG raw data, such as heart rate, heart rate variability or ECG classification. This method is robust and provides high accuracy even in case of noisy signals. Motion artefacts could be compensated on a high level. The performed study illustrates that even validated state-of-the-art R-peak detection algorithms have to be adapted and optimized for the mobile and daily usage. Due to its computational efficiency it is suitable for mobile applications in real-time.
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