Group Walking Recognition Based on Smartphone Sensors

Li, Qimeng; Gravina, Raffaele; Qiu, Sen; Wang, Zhelong; Zang, Weilin; Li, Ye

doi:10.1007/978-3-030-34833-5_8

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Our previous research [2] highlighted the limitations of using motion sensors in wearable devices, which provided insights into individual movement patterns but fell short in identifying group dynamics or shared activities. To address this, we introduce the concept of proximity or spatial closeness in our current study.…”

Section: Introductionmentioning

confidence: 99%

“…It is designed to transform community interactions and engagements by leveraging the synergy of advanced technologies. At its core, the system integrates Ultra-Wideband (UWB) position sensors [3] for precise location tracking and 9-axis motion sensors [2], [4] to capture intricate movement details. Supported by edge computing, this network excels in accuracy and responsiveness, which is essential for community-centric applications.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Li,

Mangione,

Porreca

et al. 2024

2024 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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In the rapidly evolving digital technology landscape, community-oriented wearable computing systems are emerging as a key tool for enhancing connectivity and interaction within communal spaces. This paper contributes to this burgeoning field by presenting the development and implementation of a proximity-based wearable computing testbed designed to forge stronger links within communities. The testbed exploits Ultra-Wideband (UWB) position sensors, 9-axis motion sensors, edge nodes, and a centralized server, forming a cohesive network that actively facilitates community interactions and engagements. By employing anchors and targets within the UWB sensors, the system achieves high precision in location and distance measurements, laying the groundwork for various proximity-based applications. Integrating 9-axis motion sensors and advanced edge nodes further underscores the system's versatility and robustness in wearable and edge computing. This paper delves into an in-depth exploration and evaluation of the proposed system's architecture, design, and implementation processes. It provides a comprehensive analysis of experimental results and discusses the system's potential impact on enhancing community networks, along with the future directions this technology could take.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Li,

Mangione,

Porreca

et al. 2024

2024 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

Self Cite

View full text Add to dashboard Cite

show abstract

“…Researches about Micro-Electro-Mechanical Systems (MEMS) have developed rapidly over the past decade, enabling the development of computer communication devices, high-performance physical sensors, and especially inertial sensors. These sensors are characterized by their large memory capacity, small size and low cost, and it is due to these characteristics that they are widely used in various areas [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

Liu

et al. 2021

Sensors

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Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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A Collaborative BSN-Enabled Architecture for Multi-user Activity Recognition

Gravina

et al. 2021

Internet of Things

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Group Walking Recognition Based on Smartphone Sensors

Cited by 4 publications

References 9 publications

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Development and Validation of a Proximity-based Wearable Computing Testbed for Community-oriented Wearable Systems

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

A Collaborative BSN-Enabled Architecture for Multi-user Activity Recognition

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