User-Independent Motion State Recognition Using Smartphone Sensors

Abstract

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone

“…The proposed method was independent of the expert knowledge and greatly reduced the work of manual feature design, by the selection and combination of various sensors, the results showed that the classification performance by multiple sensors can be better than that by only accelerometers. Gu et al [29] developed a novel feature, called pressure derivative, which was obtained from the barometer embedded in the smartphone to recognize the motions in the vertical plane. In addition, they added the history information of the motion modes to the classification.…”

PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

“…The method can deal with the smartphone orientation and position well but hand and bag were not considered in their study. In [19], Gu et al explored the user-independent motion recognition using smartphone sensors. Different positions such as the trouser pocket, as well as holding with or without a swinging motion were studied during Information 2016, 7, 72 4 of 18 activity recognition.…”

“…Similar to previous works [18][19][20][21][22], activity recognition can be divided into four steps: data collection, preprocessing, feature extraction and recognition. Firstly, raw data of smartphone sensors should be collected from users.…”

“…In contrast to previous studies, PACP first identifies the position of the smartphone, instead of recognizing the activity directly. For position recognition, position-sensitive features are selected, and then a decision tree-which proved to have a good performance in most previous works [19,20]-is trained for recognition. According to the result of position recognition, features extracted from acceleration are adjusted with a position parameter to improve the activity recognition accuracy.…”

“…The sliding window technique was used to split the data stream into short segments. According to the research of [19], when the window size reaches 64, it obtained the best performance. PACP finally adopted 64 as the window size with 50% overlap to segment the sensor data.…”