A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems

Abstract

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

Keywords: accelerometer; activity detection system; healthcare; inertial sensors; reweighted genetic algorithm.

Figure 1

Flow architecture of the proposed physical healthcare detection system.

Figure 2

Signal preprocessing for wearable accelerometers in the proposed healthcare model.

Figure 3

Features vectorized representation of inertial sensor stream components x, y, z.

Figure 4

The instantaneous vector magnitude for the walking signal pattern.

Figure 5

The instantaneous vector magnitude for the walking signal pattern from accelerometer sensor.

Figure 6

The three statistical features (mean, min, max) for practicing imitation in the “climbing upstairs” motion pattern using the Wireless Sensor Data Mining (WISDM) dataset.

Figure 7

Spectral Entropy for brushing hair signal pattern using the Intelligent Media-Wearable Smart Home Activities (IM-WSHA) dataset.

Figure 8

The empirical mode decomposition components from the inertial data.

Figure 9

The proposed reweighted genetic algorithm for recognizing human motion from inertial-based data.

Figure 10

The reweighted pattern-matching algorithm for human physical healthcare pattern understanding.