Adaptive Step Length Estimation Support Indoor Positioning System using Low-Cost Inertial Measurement Units

Pham, Van Thanh; Nguyen, Tuan Khai; Nguyễn, Đức Anh; Dang, Nhu Dinh; Huynh, Huu Tuê; Tran, Duc–Tan

doi:10.1109/icce48956.2021.9352113

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The step count was determined using accelerometer data ( Pham et al, 2021 ) collected from the waist of the subject during walking activity. First, we combined the raw acceleration data along the

, and

axes to form a composite magnitude signal.…”

Section: Methodsmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

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, and

axes to form a composite magnitude signal.…”

Section: Methodsmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

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Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

Cardoni,

Pau,

Rezaei

et al. 2024

Electronics

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This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g2, with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements.

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Adaptive Step Length Estimation Support Indoor Positioning System using Low-Cost Inertial Measurement Units

Cited by 2 publications

References 8 publications

Robust human locomotion and localization activity recognition over multisensory

Robust human locomotion and localization activity recognition over multisensory

Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

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