Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

Abstract

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

“…Various CNN applications were used for indoor positioning applications in [37][38][39][40]. A visual indoor positioning system was proposed in [37], where Alexnet was used to design a CNN for pedestrian activity recognition, which can serve as landmarks for indoor localization. Here, one-dimensional sensor data from accelerometers, magnetometers, gyroscopes, and barometers were considered network inputs.…”

Software engineering model based smart indoor localization system using deep-learning

“…The training phase of deep positioning was computationally intensive, the testing phase was fast and suitable for real time indoor localization. In [6], the authors designed WiFi deep, which was a Wi-Fi -based indoor fingerprinting localization system that can achieve robust and high accuracy tracking in the presence of device heterogeneity. The system employed a model regularization to enable the network to generalize and avoid over-fitting, leading to a more robust and stable models.…”

Deep CNN for Indoor Localization in IoT-Sensor Systems

“…Thus, such solutions based ML are highly recommended in real-time localization applications. This is motivated by the highly-efficient Deep Learning (DL) algorithms, which have been demonstrated to show very good performance in different contexts and applications related to the indoor localization field: LOS/NLOS identification [19,27], activity recognition [28], uncertainty prediction [29], denoising autoencoders [30], and localization [31,32]. These DL-based methods have been widely introduced into indoor localization, estimating either the location coordinates or other localization information such as room identification [31], floor identification [17], region identification [13,14,33], etc.…”