Gesture-Based Control in a Smart Home Environment

Alemuda, Fariz; Lin, Fuchun Joseph

doi:10.1109/ithings-greencom-cpscom-smartdata.2017.120

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…However, while these methods are effective in recognizing high-level activities and segmenting activity times, they fall short in identifying specific lowlevel actions or steps within an activity, which is crucial for providing real-time assistance. Closer to our actual specific proposal, Alemuda and his associates used a 6-axis wearable device to capture hand gestures and applied machine learning algorithms like decision trees and logistic regression for gesture recognition Alemuda and Lin (2017). Their method demonstrated good accuracy in controlled tests.…”

Section: Recent Existing Work Andmentioning

confidence: 99%

Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living

Doukaga,

Rakotoarson,

Roberge

et al. 2024

Preprint

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The swift progression of artificial intelligence, along with the rising demand for in-home assistance for individuals facing a loss of autonomy, has driven a significant increase in research within the domain of ambient assisted living (AAL). A key challenge challenge in developing assistive technologies in an AAL context concerns the automatic recognition of the ongoing user’s activity. Most existing approaches of Human Activity Recognition (HAR) use a level of abstraction (low granularity) that is insufficient for developing efficient assistive technologies. The majority of them focus primarily on identifying broad categories of activities, such as eating or sleeping. While this identification is sufficient for monitoring general behavior, it does not enable providing practically meaningful real-time, actionable assistance. In this paper, we propose a comparative study of two novel algorithmic approaches for hand gesture recognition, intended to serve as core components of a fine-grained activity recognition model. To this end, we have defined thirteen atomic hand gestures commonly used in cooking activities. The first model we introduce utilizes inertial data, collected from a standard wristband equipped with a triaxial accelerometer and gyroscope, and applies machine learning techniques for analysis. The second model is based on a less conventional approach, employing photoplethysmography (PPG) sensors, which are rarely used for activity recognition. We detail the design and implementation of both approaches and the conducted experiments. Finally, we present a comparative analysis of the obtained results showing the potential of such approaches for the AAL.

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Section: Recent Existing Work Andmentioning

confidence: 99%

Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living

Doukaga,

Rakotoarson,

Roberge

et al. 2024

Preprint

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show abstract

“…Gesture is a convenient and natural way to control a smart home device. (14) Gesture-based control and touchscreen technology are generally not in conflict and can even complement each other in their applications. For example, when using a smart TV, users maintain a certain distance from the TV and cannot touch the screen with their fingers.…”

Section: Operation Through Gesturesmentioning

confidence: 99%

Development of Smart Home Gesture-based Control System

Wang¹,

Lai²,

Jhuang³

et al. 2021

Sensors and Materials

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In recent years, smart speakers have been widely used to control related smart home appliances. For people with speech disabilities, smart speakers cannot be used to perform related voice control tasks. To help people with speech difficulties and effectively control related smart facilities, we use the Internet of Things to develop a smart home gesture-based control system. The prototype of the system is established by means of sensors and a control panel system. When a sensor detects a control gesture command, it can send related control signals to a controlled smart device. The controlled devices used in this study include smart home appliances such as doors and windows, air conditioners, curtains, dehumidifiers, and air purifiers. The system functions include the opening/closing of doors, windows, and curtains; the activation/ deactivation of heating and cooling systems, dehumidifiers, and air purifiers; temperature and humidity adjustment; and fan operation. With the development of this system, it is possible for people with language disabilities and elderly people to control the operation of various smart devices with gestures. It can also solve the inconvenience of needing to look for remote controls for various facilities. Moreover, it can save precious time and provide a safe, convenient, and smart home living environment for people with speech disabilities.

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“…For example, a health center can utilize a smart wristband to study patients' physical motion during their rehabilitation [1] . Inertial tracking can also be used for gesture recognition to control appliances in smart home scenarios [2] . The tracking-based text-input method can offer a more convenient way to perform text input on many small-screen or even no-screen devices, such as smartwatches and wristbands [3] .…”

Section: Introductionmentioning

confidence: 99%

“…This intuitive double-integration method can only work well under several constraints: (1) The device's orientation keeps unchanged, that is, the device has no rotations. (2) Due to the error in direction, most acceleration-based tracking methods only work well for 2D planes. (3) The tracking duration should be short [8] ; otherwise, the accumulated error can be very high.…”

Section: Introductionmentioning

confidence: 99%

Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges

Song

Cao

et al. 2021

Tsinghua Sci. Technol.

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Motion tracking via Inertial Measurement Units (IMUs) on mobile and wearable devices has attracted significant interest in recent years. High-accuracy IMU-tracking can be applied in various applications, such as indoor navigation, gesture recognition, text input, etc. Many efforts have been devoted to improving IMU-based motion tracking in the last two decades, from early calibration techniques on ships or airplanes, to recent arm motion models used on wearable smart devices. In this paper, we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices. We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.

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Gesture-Based Control in a Smart Home Environment

Cited by 12 publications

References 12 publications

Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living

Comparative Analysis of Two Methods in Fine-Grained Activity Recognition for Ambient Assisted Living

Development of Smart Home Gesture-based Control System

Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges

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