A Drone-based System for Intelligent and Autonomous Homes

Xia, Stephen; Chandrasekaran, Rishikanth; Liu, Yanchen; Yang, Chenye; Rosing, Tajana; Jiang, Xiaofan

doi:10.1145/3485730.3492881

Cited by 14 publications

(3 citation statements)

References 3 publications

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“…Opportunity: While there are works that utilize multiple sensors to improve robustness, they require users to wear or have access to all of the sensors they utilize. An exciting new direction is to create generalized frameworks that can adaptively utilize data from whatever sensors happen to be available, such as a smart home intelligence system [315]. For this direction, deep learning methods seem more suitable than classical machine learning methods because neural networks can be more easily tuned and adapted to different domains (i.e., different sensors) than rigid classical models, just by tuning weights or by mixing and matching different layers or embeddings.…”

Section: Model Robustnessmentioning

confidence: 99%

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

Zhang

et al. 2022

Sensors

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250

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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: Model Robustnessmentioning

confidence: 99%

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

Zhang

et al. 2022

Sensors

Self Cite

250

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“…Afterwards, the drone can pick up a WEM equipped with a powerful light that it can use to guide survivors out using the paths it mapped. This low-cost, flexible, secure, and easy-to-install system is an important step towards creating smarter environments, such as offices, cities, and homes [3].…”

Section: Introductionmentioning

confidence: 99%

A modular and reconfigurable sensing and actuation platform for smarter environments and drones

Zhao

Liu

Dhupar

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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There has been an immense growth in sensors, actuators, and smart devices in recent years, which enable us to better sense, actuate, and understand the physical world. Despite this growth, we have yet to achieve fully intelligent environments. This is, in part, due to the large number of different organizations creating smart devices with proprietary technologies and communication protocols that are not compatible with each other and require significant engineering to incorporate and adapt to specific applications. In this work, we present an easy-to-install and low-cost embedded platform that allows users to rapidly configure a mixture of sensors and actuators. The system is based on the commonly-used Raspberry Pi ecosystem, easily configurable, and does not require users to have prior knowledge of programming, which allows anyone, regardless of background, to use. We also introduce a battery-powered wireless extension module that is suitable for mobile drone applications, where a chord-powered Raspberry Pi is not suitable. We demonstrate the impact our system has on enabling drones with flexible sensing modalities and creating smarter environments by integrating our platform into a variety of intelligent home applications. CCS CONCEPTS• Computer systems organization → Sensor networks; Sensors and actuators.

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“…In recent years, we have seen a drastic fall in price of and size of drones. As such, we envision that drones will be utilized as a powerful mobile sensing and actuation platform for a variety of indoor applications [5], not only outdoors.…”

mentioning

confidence: 99%

A sensorless drone-based system for mapping indoor 3D airflow gradients

Liu

Zhao

Xia

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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With the global spread of the COVID-19 pandemic, ventilation indoors is becoming increasingly important in preventing the spread of airborne viruses. However, while sensors exist to measure wind speed and airflow gradients, they must be manually held by a human or an autonomous vehicle, robot, or drone that moves around the space to build an airflow map of the environment. In this demonstration, we present DAE, a novel drone-based system that can automatically navigate and estimate air flow in a space without the need of additional sensors attached onto the drone. DAE directly utilizes the flight controller data that all drones use to self-stabilize in the air to estimate airflow. DAE estimates airflow gradients in a room based on how the flight controller adjusts the motors on the drone to compensate external perturbations and air currents, without the need for attaching additional wind or airflow sensors. CCS CONCEPTS• Computer systems organization → Sensor networks; Sensors and actuators.

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A Drone-based System for Intelligent and Autonomous Homes

Cited by 14 publications

References 3 publications

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

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

A modular and reconfigurable sensing and actuation platform for smarter environments and drones

A sensorless drone-based system for mapping indoor 3D airflow gradients

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