A Smartphone-Based System for Improving Pedestrian Safety

Xia, Stephen; Godoy, Daniel de; Islam, Bashima; Islam, Tamzeed; Nirjon, Shahriar; Kinget, Peter R.; Jiang, Xiaofan

doi:10.1109/vnc.2018.8628320

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…Many of the works discussed leverage a single platform (i.e., either a smartphone or ASIC), but there are still many opportunities for improving the practical use of HAR by exploring intelligent ways to partition computation across the cloud, mobile platforms, and other edge devices. DNN-based HAR systems can largely benefit by incorporating methodologies proposed by works such as [325][326][327][328][329][330][331][332], that carefully partition computation and data across multiple devices and the cloud.…”

Section: Challenges In Model Deploymentmentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

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212

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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Section: Challenges In Model Deploymentmentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

Self Cite

212

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“…Additionally, the number of smartphone application downloads is expected to increase from 178 billion in 2017 to 258 billion by 2022, and the total revenue generated from these applications is expected to grow from 88 billion USD in 2016 to more than 180 billion USD by the end of 2020 [31]. The increase in smartphones and smartphone usage has also spurred on numerous mobile wearable platforms for various applications such as safety [3,7,8,35,36] and health monitoring [15,21].…”

mentioning

confidence: 99%

Pams

Xia

Jiang

2020

Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Thi

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Smartphones and mobile applications have become an integral part of our daily lives. This is reflected by the increase in mobile devices, applications, and revenue generated each year. However, this growth is being met with an increasing concern for user privacy, and there have been many incidents of privacy and data breaches related to smartphones and mobile applications in recent years. In this work, we focus on improving privacy for audio-based mobile systems. These applications will generally listen to all sounds in the environment and may record privacy-sensitive signals, such as speech, that may not be needed for the application. We present PAMS, a software development package for mobile applications. PAMS integrates a novel sound source filtering algorithm called Probabilistic Template Matching to generate a set of privacy-enhancing filters that remove extraneous sounds using learned statistical "templates" of these sounds. We demonstrate the effectiveness of PAMS by integrating it into a sleep monitoring system, with the intent to remove extraneous speech from breathing, snoring, and other sleep sounds that the system is monitoring. By comparing our PAMS enhanced sleep monitoring system with existing mobile systems, we show that PAMS can reduce speech intelligibility by up to 74.3% while maintaining similar performance in detecting sleeping sounds. CCS CONCEPTS • Computer systems organization → Sensor networks; • Security and privacy → Domain-specific security and privacy architectures; • Human-centered computing → Ubiquitous and mobile computing systems and tools.

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