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The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented a unique opportunity to the activity-recognition community to test their approaches on a large, real-life benchmark dataset with activities different from those typically recognized. The goal of the challenge was to recognize, as accurately as possible, eight locomotion activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway) using smartphone sensor data. This paper describes the method we developed to win this challenge, and provides an analysis of the effectiveness of its components. We used complex feature extraction and selection methods to train classical machine learning models. In addition, we trained deep learning models using a novel end-to-end architecture for deep multimodal spectro-temporal fusion. All the models were fused into an ensemble with the final predictions smoothed by a hidden Markov model to account for temporal dependencies of the activities. The presented method achieved an F1 score of 94.9% on the challenge test data. We tested different sampling frequencies, window sizes, feature types, classification models and the importance of stand-alone sensors and their fusion for the task. Finally, we present an energy-efficient smartphone implementation of the method.

We present the concept of an Intelligent Assistant Carer system for the elderly, designed to help with active aging and to facilitate the interactions with carers. The system is modular, allowing the users to choose the appropriate functions according to their needs, and is built on an open platform in order to make it compatible with third-party products and services. Currently, the system consists of a wearable device (a smartwatch) and an internet portal that manages the data and takes care of the interactions between the user, the carers, and the support services. We present in detail one of the modules, i.e., fall detection, and the results of a pilot study for the system on 150 users over the course of 3 months.

The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented a unique opportunity to the activity-recognition community to test their approaches on a large, real-life benchmark dataset with activities different from those typically being recognized. The goal of the challenge was to recognize eight locomotion activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). This chapter describes the submissions winning the first and second place. They both start with data preprocessing, including a normalization of the phone orientation. Then, a wide set of hand-crafted domain features in both frequency and time domain are computed and their quality evaluated. The second-place submission feeds the best features into an XGBoost machine-learning model with optimized hyper-parameters, achieving the accuracy of 90.2%. The first-place submission builds an ensemble of models, including deep learning models, and finally refines the ensemble’s predictions by smoothing with a Hidden Markov model. Its accuracy on an internal test set was 96.0%.

In recent years, activity recognition (AR) has become prominent in ubiquitous systems. Following this trend, the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge provides a unique opportunity for researchers to test their AR methods against a common, real-life and large-scale benchmark. The goal of the challenge is to recognize eight everyday activities including transit. Our team, JSI-Deep, utilized an AR approach based on combining multiple machine-learning methods following the principle of multiple knowledge. We first created several base learners using classical and deep learning approaches, then integrated them into an ensemble, and finally refined the ensemble's predictions by smoothing. On the internal test data, the approach achieved 96% accuracy, which is a significant leap over the baseline 60%.

The Sussex-Huawei Locomotion-Transportation recognition challenge presents a unique opportunity to the activity-recognition community - providing a large, real-life dataset with activities different from those typically being recognized. This paper describes our submission (team JSI Classic) to the competition that was organized by the dataset authors. We used a carefully executed machine learning approach, achieving 90% accuracy classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was data preprocessing, including a normalization of the phone orientation. Then, a wide set of hand-crafted domain features in both frequency and time domain were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen (XGBoost) and its hyper-parameters were optimized. The recognition result for the testing dataset will be presented in the summary paper of the challenge [13].

Information technologies can assist the elderly in their daily lives and thus improve their quality of life. Here, we present a system that was developed within the framework of the EU H2020 project IN LIFE to make the elderly feel more secure. The system consists of a smartwatch, a tablet, and a web portal for carers. The smartwatch has several functionalities; the most important are automatic fall detection, SOS button, communication with carers, location detection in case of emergency, and general activity monitoring. The smartwatch communicates with the server via GSM, therefore it does not depend on wireless internet or Bluetooth connection. The tablet acts as a virtual doorman and also as an interface for the carer, while the portal acts as a central information hub for notifications, scheduling of tasks, and user overview. We present the initial feedback from the users in a pilot study and discuss some modifications that we carried out considering this feedback.

The constant increase of the elderly population and the need to prolong the independent live, is driving the development of telecare systems. Two of the key factors of such systems are their simplicity of usage and minimal obtrusiveness. Therefore, in this paper we propose a practical, intuitive and simple smartwatch-based telecare system. The system has several functionalities, including: automatic fall detection, activity analysis, SOS red button, location information, and reminders. The proposed system can work indoors as well as outdoors, and is completely independent, i.e., it requires only a SIM card to function and does not depend on base stations, internet connection, Bluetooth and similar. The evaluation of the fall detection algorithm showed that it detects all of the fast falls and minimizes the false positives, achieving 85% accuracy.