A modern vehicle contains a large number of electronic control units and sensors that are connected to cloud environments. These electronic units generate a huge amount of data that can be leveraged to identify the current driver and adapt to their behavior. For example, driver identification can improve the accuracy of range estimation in battery electric vehicles. This study focuses on identifying drivers based on their behaviour by using multivariate time series data acquired by the sensors available in the vehicle. We propose two different classifiers: one based on Bi-directional stacked Long Short-Term Memory with Attention mechanism (BiLSTM-A) and another based on a Modified Time Series Transformer (MTST). We perform driver identification in two different ways: 1) by applying a classifier on a single time segment; and 2) by using our proposed time-shift ensembles to combine predictions made from multiple time segments. We experimentally evaluate the proposed techniques on a publicly available dataset that comprises 10 drivers. By using all appropriate features, the proposed BiLSTM-A and MTST classifiers identify the driver with an accuracy of 55% and 73%, respectively, from a single time segment of 60 seconds duration. Time-shift ensembles increase the accuracy substantially, to 92% for BiLSTM-A and 97% for MTST when a 460-second time period is used for each prediction. We conclude that the proposed MTST classifier outperforms BiLSTM-A and existing classifiers in the driver identification task, and time-shift ensembles substantially improve the accuracy.

We propose a new methodology to attain invariance to the positioning of body-worn motion-sensor units for recognizing everyday and sports activities. We first consider random interchangeability of the sensor units so that the user does not need to distinguish between them before wearing. To this end, we propose to use the compact singular value decomposition (SVD) that significantly reduces the accuracy degradation caused by random interchanging of the units. Second, we employ three variants of a generalized classifier that requires wearing only a single sensor unit on any one of the body parts to classify the activities. We combine both approaches with our previously developed methods to achieve invariance to both position and orientation, which ultimately allows the user significant flexibility in sensor-unit placement (position and orientation). We assess the performance of our proposed approach on a publicly available activity data set recorded by body-worn motion-sensor units. The experimental results suggest that there is a tolerable reduction in accuracy, which is justified by the significant flexibility and convenience offered to users when placing the units.

We propose techniques that achieve invariance to the positioning of wearable motion sensor units on the body for the recognition of daily and sports activities. Using two sequence sets based on the sensory data allows each unit to be placed at any position on a given rigid body part. As the unit is shifted from its ideal position with larger displacements, the activity recognition accuracy of the system that uses these sequence sets degrades slowly, whereas that of the reference system (which is not designed to achieve position invariance) drops very fast. Thus, we observe a tradeoff between the flexibility in sensor unit positioning and the classification accuracy. The reduction in the accuracy is at acceptable levels, considering the convenience and flexibility provided to the user in the placement of the units. We compare the proposed approach with an existing technique to achieve position invariance and combine the former with our earlier methodology to achieve orientation invariance. We evaluate our proposed methodology on a publicly available data set of daily and sports activities acquired by wearable motion sensor units. The proposed representations can be integrated into the preprocessing stage of existing wearable systems without significant effort.

This work investigates inter-subject and inter-activity variability of a given activity dataset and provides some new definitions to quantify such variability. The definitions are sufficiently general and can be applied to a broad class of datasets that involve time sequences or features acquired using wearable sensors. The study is motivated by contradictory statements in the literature on the need for user-specific training in activity recognition. We employ our publicly available dataset that contains 19 daily and sports activities acquired from eight participants who wear five motion sensor units each. We pre-process recorded activity time sequences in three different ways and employ absolute, Euclidean and dynamic time warping distance measures to quantify the similarity of the recorded signal patterns. We define and calculate the average inter-subject and inter-activity distances with various methods based on the raw and pre-processed time-domain data as well as on the raw and pre-processed feature vectors. These definitions allow us to identify the subject who performs the activities in the most representative way and pinpoint the activities that show more variation among the subjects. We observe that the type of pre-processing used affects the results of the comparisons but that the different distance measures do not alter the comparison results as much. We check the consistency of our analysis and results by highlighting some of our activity recognition rates based on an exhaustive set of sensor unit, sensor type and subject combinations. We expect the results to be useful for dynamic sensor unit/type selection, for deciding whether to perform user-specific training and for designing more effective classifiers in activity recognition.