SASMOTE: A Self-Attention Oversampling Method for Imbalanced CSI Fingerprints in Indoor Positioning Systems

Liu, Ankang; Cheng, Lingfei; Yu, Changdong

doi:10.3390/s22155677

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…A combination of augmentation techniques has been applied in indoor localization [ 62 , 63 ] including adding information to the reference dataset and deep learning-based approach [ 34 , 64 ], yet these involve computational complexities. Few studies have focused on using methods based on signal patterns between sensors to augment training data specifically for indoor localization purposes in nursing homes.…”

Section: Related Literaturementioning

confidence: 99%

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2024

Sensors

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In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

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Section: Related Literaturementioning

confidence: 99%

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2024

Sensors

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“…On the other hand, CSI represents most detailed physical layer information compared to RSSI and BLE, providing channel information such as amplitude and phase on the subcarriers. CSI has been employed for person localization as can be seen in [ 13 , 14 , 15 ], for sign detection developed in [ 16 ], gesture detection in [ 17 ], human activity recognition as shown in [ 18 , 19 ], and person recognition such as WifiU [ 20 ] analyzes unique variations in the CSI on the Wi-Fi receiver, and even intrusion detection such as APID in [ 21 ]. CSI does not require any additional applications or use additional devices to detect human presence, unlike BLE deployments that usually require the installation of several BLE beacons in different locations to function properly, as the coverage area of BLE beacons is smaller than WiFi zones.…”

Section: Introductionmentioning

confidence: 99%

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks

Mesa-Cantillo

Sánchez-Rodríguez

Alonso-González

et al. 2023

Sensors

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In recent times, we have been witnessing the development of multiple applications and deployment of services through the indoors location of people as it allows the development of services of interest in areas related mainly to security, guiding people, or offering services depending on their localization. On the other hand, at present, the deployment of Wi-Fi networks is so advanced that a network can be found almost anywhere. In addition, security systems are more demanded and are implemented in many buildings. Thus, in order to provide a non intrusive presence detection system, in this manuscript, the development of a methodology is proposed which is able to detect human presence through the channel state information (CSI) of wireless communication networks based on the 802.11n standard. One of the main contributions of this standard is multiple-input multiple-output (MIMO) with orthogonal frequency division multiplexing (OFDM). This makes it possible to obtain channel state information for each subcarrier. In order to implement this methodology, an analysis and feature extraction in time-domain of CSI is carried out, and it is validated using different classification models trained through a series of samples that were captured in two different environments. The experiments show that the methodology presented in this manuscript obtains an average accuracy above 90%.

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Sida

Zhang,

Dai,

Chen

et al. 2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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The coronavirus disease 2019 (COVID-19) pneumonia still persists and its chief complaint is dry cough. Physicians design wireless stethoscopes to facilitate diagnosis, however, lung sounds could be easily interfered with by external noises. To achieve lung sound enhancement, prior researches mostly assume the amount of clean and noisy data are the same. This assumption is hardly met due to extensive labor effort for data collection and annotation. The data imbalance across domains widely happens in real-world IoT systems, e.g. sound enhancement and WiFi-based human sensing. In this paper, we propose SIDA, a self-supervised imbalanced domain adaptation framework for sound enhancement and WiFi sensing, which makes it a generic time series domain adaptation solution for IoT systems. SIDA proposes a self-supervised imbalanced domain adaptation model that separately learns the representation of time series signals in a minority domain with limited samples, a majority domain with rich samples, and their mapping relations. For lung sound enhancement, we further proposes a phase correction model to sanitize the phase and a SNR prediction algorithm to recursively perform domain adaptation in an imbalanced noisy and clean lung sound dataset. Extensive experiments demonstrate SIDA increases noisy samples' SNR by 16.49dB and 4.06dB on a synthetic and a realistic imbalanced lung sound dataset, respectively. For WiFi-based human sensing, SIDA designs a cross-domain WiFi-based human identification model irrespective of walking trajectory. A specific trajectory where a group of people walks along in a realistic testing environment is considered the minority domain, and several other trajectories are stored at a server as the majority domain. Extensive experiments show SIDA could recognize individuals with an average accuracy of 94.72% and significantly outperform baselines on highly imbalanced WiFi dataset in cross-domain human identification tasks.

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SASMOTE: A Self-Attention Oversampling Method for Imbalanced CSI Fingerprints in Indoor Positioning Systems

Cited by 4 publications

References 36 publications

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks

Sida

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