Human Activity Recognition based on Real Life Scenarios

Machot, Fadi Al; Ranasinghe, Suneth; Plattner, Johanna; Jnoub, Nour

doi:10.1109/percomw.2018.8480138

Cited by 21 publications

(23 citation statements)

References 16 publications

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“…Unbalanced data processing methods are used to address the problem of class imbalance (i.e. when there is a disproportionate ratio of instances in each class) [29]. This is often the case with real world data, and the models learned from them generally have a good accuracy on the majority class but perform poorly on other classes.…”

Section: Data Augmentation Methods Create Additional Training Samplesmentioning

confidence: 99%

“…In [28], the authors use data augmentation methods such as Gaussian noise to artificially create new training data from existing learning data. In [29], the authors present a Recurrent Neural Network (RNN) based on a windowing approach for human activity recognition. They apply a synthetic minority over sampling technique to deal with the class imbalance problem.…”

Section: Data Augmentation Methods Create Additional Training Samplesmentioning

confidence: 99%

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Machine Learning for a Context Mining Facility

Rabah

Pinheiro²,

Grand³

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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This paper considers generalizing context reasoning capabilities through a context mining facility offered to all Information System applications. This facility requires mining context data at the system scale, which raises several challenges for Machine Learning approaches used for such mining. Through a detailed literature review, we analyze these approaches with regard to the requirements of such a context mining facility at the Information System level, pointing to the potential and to the challenges raised by this perspective.

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Section: Data Augmentation Methods Create Additional Training Samplesmentioning

confidence: 99%

Section: Data Augmentation Methods Create Additional Training Samplesmentioning

confidence: 99%

Machine Learning for a Context Mining Facility

Rabah

Pinheiro²,

Grand³

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

View full text Add to dashboard Cite

show abstract

“…More recently, paper [18] used vanilla RNN in a learning approach for HAR since human activities generate sequences that contain time-dependent sensor records. Before the trading the RNN model phase, they applied dynamic windowing approach in which each window contains the best fitting sensor set for each activity; the obtained windows are then feed the RNN classification model.…”

Section: A Activity Recognition Using Deep Learning-based Approachesmentioning

confidence: 99%

Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Alghamdi¹,

Fadel²,

Alowidi³

2021

IJACSA

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Human activity recognition is considered a challenging task in sensor-based monitoring systems. In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants. Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly. Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition. Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes. Our model contains several onedimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types. For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90). We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living. We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions. The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.

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“…Data capturing can be conducted by various wearable sensors, such as an accelerometer [3,14,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32], a gyroscope [15,21,28,31,32,33,34,35,36], a magnetometer [19,34,35,36,37,38], an Electrocardiogram sensor (ECG) [31,39], a Global Positioning System (GPS) sensor [22], Electromyography sensor (EMG) [8,40,41], etc. Besides wearable sensors, data collection can be conducted using non-contact sensing [4,42,43,44], and with various sensors that are integrated into smartphones [45,46,47,48,49,50]. Many problems during data collection from wearable sensors may occur.…”

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

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Human Activity Recognition based on Real Life Scenarios

Cited by 21 publications

References 16 publications

Machine Learning for a Context Mining Facility

Machine Learning for a Context Mining Facility

Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

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