Locomotion and Transportation Mode Recognition from GPS and Radio Signals: Summary of SHL Challenge 2021

Wang, Lin; Ciliberto, Mathias; Gjoreski, Hristijan; Lago, Paula; Murao, Kazuya; Okita, Tsuyoshi; Roggen, Daniel

doi:10.1145/3460418.3479373

Cited by 23 publications

(3 citation statements)

References 42 publications

(30 reference statements)

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“…Our proposed MLP architecture [91][92][93][94] was designed to handle the complexity and variability inherent in the sensor data collected. With the manual feature extraction and subsequent optimization processes we employed, our MLP [95][96][97][98] was strategically tasked with classifying a refined feature vector that encapsulates essential information for robust activity recognition.…”

Section: Proposed Multi-layer Perceptron Architecturementioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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Section: Proposed Multi-layer Perceptron Architecturementioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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“…However, the lack of good features caused lower accuracy rates. In Wang et al [ 13 ], a study has explained the locomotion activity recognition method for different activities. They have used radio sensor data to recognize locomotion and transportation actions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Mudawi

Alabduallah

Jalal

et al. 2023

Sensors

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Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.

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“…At the time of writing this paper, the SHL 2021 is going on, which aims at transportation mmode recognition using GPS and radio sensors(Wang et al, 2021).Frontiers in Computer Science | www.frontiersin.org September 2021 | Volume 3 | Article 713719…”

mentioning

confidence: 99%

Three-Year Review of the 2018–2020 SHL Challenge on Transportation and Locomotion Mode Recognition From Mobile Sensors

Wang

Gjoreski

Ciliberto

et al. 2021

Front. Comput. Sci.

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The Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenges aim to advance and capture the state-of-the-art in locomotion and transportation mode recognition from smartphone motion (inertial) sensors. The goal of this series of machine learning and data science challenges was to recognize eight locomotion and transportation activities (Still, Walk, Run, Bus, Car, Train, Subway). The three challenges focused on time-independent (SHL 2018), position-independent (SHL 2019) and user-independent (SHL 2020) evaluations, respectively. Overall, we received 48 submissions (out of 93 teams who registered interest) involving 201 scientists over the three years. The survey captures the state-of-the-art through a meta-analysis of the contributions to the three challenges, including approaches, recognition performance, computational requirements, software tools and frameworks used. It was shown that state-of-the-art methods can distinguish with relative ease most modes of transportation, although the differentiating between subtly distinct activities, such as rail transport (Train and Subway) and road transport (Bus and Car) still remains challenging. We summarize insightful methods from participants that could be employed to address practical challenges of transportation mode recognition, for instance, to tackle over-fitting, to employ robust representations, to exploit data augmentation, and to exploit smart post-processing techniques to improve performance. Finally, we present baseline results to compare the three challenges with a unified recognition pipeline and decision window length.

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Locomotion and Transportation Mode Recognition from GPS and Radio Signals: Summary of SHL Challenge 2021

Cited by 23 publications

References 42 publications

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Three-Year Review of the 2018–2020 SHL Challenge on Transportation and Locomotion Mode Recognition From Mobile Sensors

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