Indoor/Outdoor Switching Detection Using Multisensor DenseNet and LSTM

Zhu, Yida; Luo, Haiyong; Zhao, Fang; Chen, Runze

doi:10.1109/jiot.2020.3013853

Cited by 30 publications

(26 citation statements)

References 27 publications

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“…Knowledge of a user's environment has been recently leveraged to develop healthcare applications [7], [8], design seamless navigation systems [9]- [11], and augment the efficiency of wireless network operation [12], [13]. Hence, a significant effort has been made to develop accurate and efficient indoor outdoor detection (IOD) models [14]- [20].…”

Section: Introductionmentioning

confidence: 99%

“…The fixed values are typically determined empirically through measurement campaigns. In the second case, the mobile phone sensor measurements are exploited to train ML algorithms, which learn to infer a user's environment [15], [17], [18], [20]. Threshold-based models are lightweight, easy to implement and interpret, however they lack scalability and their accuracy can deteriorate significantly in environments other than those in which the measurement campaigns take place [15].…”

Section: Introductionmentioning

confidence: 99%

“…More importantly, once trained offline, ML-based models can be employed in real-time to infer a user's environment in a computationally efficient manner, since their predictions are based only on simple operations between matrices. The fact that the main computational burden of ML-based models resides in their offline training stage, along with their augmented generalizability, has fostered their wider adoption, and hence they have lately attracted greater scientific interest [15], [17], [18], [20]- [25]. 2 The viability of ML-based IOD models has alredy been proved for several applications critical for the IoT ecosystem, such as seamless navigation systems and healthcare services [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, when a user enters a building it should be expected that the received cellular signal power will exhibit a rapid fall. Thus, lately there have been attempts to exploit the temporal nature of the problem, considering sensor information not only from the current state, but from previous states as well [19], [20]. In [20], a long short-term memory (LSTM)-based model is employed to determine a user's environment.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep-Learning-Based Multivariate Time-Series Classification for Indoor/Outdoor Detection

Bakirtzis

Qiu

Wassell

et al. 2022

IEEE Internet Things J.

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Recently, the topic of indoor outdoor detection (IOD) has seen its popularity increase, as IOD models can be leveraged to augment the performance of numerous Internet of Things and other applications. IOD aims at distinguishing in an efficient manner whether a user resides in an indoor or an outdoor environment, by inspecting the cellular phone sensor recordings. Legacy IOD models attempt to determine a user's environment by comparing the sensor measurements to some threshold values. However, as we also observe in our experiments, such models exhibit limited scalability, and their accuracy can be poor. Machine learning (ML)-based IOD models aim at removing this limitation, by utilizing a large volume of measurements to train ML algorithms to classify a user's environment. Yet, in most of the existing research, the temporal dimension of the problem is disregarded. In this paper, we propose treating IOD as a multivariate time series classification (TSC) problem, and we explore the performance of various deep learning (DL) models. We demonstrate that a multivariate TSC approach can be used to monitor a user's environment, and predict changes in its state, with greater accuracy compared to conventional approaches that ignore the feature variation over time. Additionally, we introduce a new DL model for multivariate TSC, exploiting the concept of self-attention and atrous spatial pyramid pooling. The proposed DL multivariate TSC framework exploits only low power consumption sensors to infer a user's environment, and it outperforms state-of-the-art models, yielding a higher accuracy combined with a smaller computational cost.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep-Learning-Based Multivariate Time-Series Classification for Indoor/Outdoor Detection

Bakirtzis

Qiu

Wassell

et al. 2022

IEEE Internet Things J.

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show abstract

“…The user's HAR context expresses semantic information about real-time user activity from the original sensor signal sequence. User activity information is an essential context for many applications, such as smart homes [1], [2], human-computer interaction [3], health monitoring [4], [5], transportation schedules [6], [7], [8], etc. Those applications require real-time response and accurate HAR performance.…”

Section: Introductionmentioning

confidence: 99%

A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation

Chen¹,

Luo²,

Zhao³

et al. 2021

Preprint

Self Cite

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Human activity recognition (HAR) based on IMU sensors is an essential domain in ubiquitous computing. Because of the improving trend to deploy artificial intelligence into IoT devices or smartphones, more researchers design the HAR models for embedded devices. We propose a plug-and-play HAR modeling pipeline with multi-level distillation to build deep convolutional HAR models with native support of embedded devices. SMLDist consists of stage distillation, memory distillation, and logits distillation, which covers all the information flow of the deep models. Stage distillation constrains the learning direction of the intermediate features.Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks. Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target. We compare the performance of accuracy, F1 macro score, and energy cost on the embedded platform of various state-of-the-art HAR frameworks with a MobileNet V3 model built by SMLDist. The produced model has well balance with robustness, efficiency, and accuracy. SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.

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Prediction of UWB Positioning Coordinates with or Without Interference Based on SVM

Yang

Wang

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Indoor/Outdoor Switching Detection Using Multisensor DenseNet and LSTM

Cited by 30 publications

References 27 publications

Deep-Learning-Based Multivariate Time-Series Classification for Indoor/Outdoor Detection

Deep-Learning-Based Multivariate Time-Series Classification for Indoor/Outdoor Detection

A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation

Prediction of UWB Positioning Coordinates with or Without Interference Based on SVM

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