SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Ramos, Raúl Gómez; Domingo, Jaime Duque; Zalama, Eduardo; Gómez-García-Bermejo, Jaime; López, Joaquín

doi:10.3390/s22218109

Cited by 13 publications

(9 citation statements)

References 57 publications

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“…The authors used the CASAS database (Cairo, Milan, Kyoto7, Kyoto8 and Kyoto11) to obtain an accuracy between 86.68% and 97.08%. In [72], the authors developed a database (SDHAR-HOME) with a total of 18 activities, whose measurements correspond to signals from environmental sensors located in a real home inhabited by two people. The authors applied three DL approaches: RNN [73], LSTM and GRU, obtaining a hit rate of 90.91%.…”

Section: Summary Of the Main Har Supervised Learning Methodsmentioning

confidence: 99%

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An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Gómez-Ramos,

Duque-Domingo,

Zalama

et al. 2023

Electronics

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As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time.

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Section: Summary Of the Main Har Supervised Learning Methodsmentioning

confidence: 99%

“…The database chosen to perform the study is SDHAR-HOME [72]. It was built in a real home where two people live together.…”

Section: Sdhar-home Dataset: Analysis and Descriptionmentioning

confidence: 99%

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Gómez-Ramos,

Duque-Domingo,

Zalama

et al. 2023

Electronics

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“…The work by Wang et al [ 40 ] provides the usage of CNN and LSTM altogether to get much better results. Ramos et al [ 41 ] used RNN, LSTM and GRU to get real-time detection of human activities. A one-dimensional Convolutional Neural Network with a bidirectional long short-term memory (1D-CNN-BiLSTM) model was presented by Luwe et al [ 42 ] which results in a much better accuracy of 94.17% to all other recent works in HAR using DL.…”

Section: Literaure Surveymentioning

confidence: 99%

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

Khan

Imaduddin

Singh

et al. 2023

Sensors

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The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

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“…Augmented reality refers to a medium in which digital information is added or superimposed onto the real world in accordance with the world itself and shown to a user depending on their position and perspective [ 40 ]. In an interactive simulation, human behavior is used as the basis for interaction, rather than deliberate interaction, allowing users to further integrate into the experience and enhance the immersion of the interaction through a variety of different behaviors, as well as by performing system functions in a wearable manner [ 41 , 42 , 43 , 44 , 45 ].…”

Section: Related Researchmentioning

confidence: 99%

SENS+: A Co-Existing Fabrication System for a Smart DFA Environment Based on Energy Fusion Information

Chang

Huang

Hong

et al. 2023

Sensors

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In factories, energy conservation is a crucial issue. The co-fabrication space is a modern-day equivalent of a new factory type, and it makes use of Internet of Things (IoT) devices, such as sensors, software, and online connectivity, to keep track of various building features, analyze data, and produce reports on usage patterns and trends that can be used to improve building operations and the environment. The co-fabrication user requires dynamic and flexible space, which is different from the conventional user’s usage. Because the user composition in a co-fabrication space is dynamic and unstable, we cannot use the conventional approach to assess their usage and rentals. Prototyping necessitates a specifically designed energy-saving strategy. The research uses a “seeing–moving–seeing” design thinking framework, which enables designers to more easily convey their ideas to others through direct observation of the outcomes of their intuitive designs and the representation of their works through design media. The three components of human behavior, physical manufacture, and digital interaction are primarily the focus of this work. The computing system that connects the physical machine is created through communication between the designer and the digital interface, giving the designer control over the physical machine. It is an interactive fabrication process formed by behavior. The Sensible Energy System+ is an interactive fabrication process of virtual and real coexistence created by combining the already-existing technology, the prototype fabrication machine, and SENS. This process analyzes each step of the fabrication process and energy, fits it into the computing system mode to control the prototype fabrication machine, and reduces the problem between virtual and physical fabrication and energy consumption.

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SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Cited by 13 publications

References 57 publications

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

SENS+: A Co-Existing Fabrication System for a Smart DFA Environment Based on Energy Fusion Information

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