Efficient dense labelling of human activity sequences from wearables using fully convolutional networks

Yao, Rui; Lin, Guosheng; Shi, Qinfeng; Ranasinghe, Damith C.

doi:10.1016/j.patcog.2017.12.024

Cited by 91 publications

(69 citation statements)

References 14 publications

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“…More recently, Rui Yao et al proposed a human activity recognition algorithm based on full convolutional neural network [35], which realized the dense prediction of human activity sequences from wearables and conducted extensive experiments on three datasets. Their experiments obtain 88.7% with weighted F-measure on Opportunity Locomotion dataset, 59.6% on Opportunity Gesture dataset, 89.3% on subject 1 for Hand Gesture dataset, 88.3% on subject 2 for Hand Gesture dataset, and 79.0% on the self-collected Hospital dataset.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on Motion Sensor Using U-Net

Zhang

et al. 2019

IEEE Access

102

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Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method. This method faces the multi-class window problem which mistakenly labels different classes of sampling points within a window as a class. In this paper, a HAR algorithm based on U-Net is proposed to perform activity labeling and prediction at each sampling point. The activity data of the triaxial accelerometer is mapped into an image with the single pixel column and multi-channel which is input into the U-Net network for training and recognition. Our proposal can complete the pixellevel gesture recognition function. The method does not need manual feature extraction and can effectively identify short-term behaviors in long-term activity sequences. We collected the Sanitation dataset and tested the proposed scheme with four open data sets. The experimental results show that compared with Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully Convolutional Networks (FCN) methods, our proposal has the highest accuracy and F1-socre in each dataset, and has stable performance and high robustness. At the same time, after the U-Net has finished training, our proposal can achieve fast enough recognition speed. IntroductionHuman activity recognition (HAR) is the key technology of human-computer interaction and human activity analysis. The basic task of HAR is to select the appropriate sensor and deploy it to monitor and capture the user's activity [1] . HAR can be divided into two categories. The first one is video-based HAR, using video cameras to monitor the activity of the human body. Another is sensor-based HAR, which is based on time series data collected by sensors such as mobile phone built-in accelerometers [2][3][4] , wrist-worn accelerometers [5][6][7] , waist-mounted accelerometers [8][9] , gyroscopes and magnetometers [10] . Due to the wide use of portable and wearable sensors with low cost, low power consumption, high capacity and miniaturization, HAR based on sensor data has become a research hotspot. The HAR system can be used in human-computer interaction application [11] , behavior monitoring [12][13] , health monitoring [14] , smart home [15] , medical care [16][17] and so on.Data collected from portable and wearable sensors are usually time series data. Human activity recognition for time series is a complex process, which usually involves the following steps. First, preprocess the time series data such as smoothing, normalization [6] , and separating gravity component [18] from acceleration data. Then segment the

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Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on Motion Sensor Using U-Net

Zhang

et al. 2019

IEEE Access

102

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“…Examining the entire literature corpus, it is striking that a majority of 38 contributions utilises individual datasets, and 34 of them do so without referring to data that are publicly available in repositories. Thus, there are four contributions using both individual and repository data [22,54,71,83]. As most contributions lack a recording protocol and a detailed description of the recorded activities, it remains unclear whether the motion patterns and their assigned activity labels as well as the underlying activity definitions are comparable.…”

Section: Datasetmentioning

confidence: 99%

“…They have become the state-of-the-art method for solving HAR problems in the context of gesture recognition, activities of daily living (ADL) as well as in industrial settings [11,21,22].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

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This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner's perspective.

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“…We used a 1x1 Conv layer as the last layer. Similar to [24] we kept the stride of the Conv and Max-pool layers as 1 to ensure the output size matches the input size. Please see [28] for further details.…”

Section: Conv Networkmentioning

confidence: 99%

Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits

Chesser

Jayatilaka

Visvanathan

et al. 2019

2019 IEEE International Conference on Pervasive Computing and Communications (PerCom

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Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometerbased RFID sensor embodiment-ID-Sensor-for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.

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Efficient dense labelling of human activity sequences from wearables using fully convolutional networks

Cited by 91 publications

References 14 publications

Human Activity Recognition Based on Motion Sensor Using U-Net

Human Activity Recognition Based on Motion Sensor Using U-Net

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits

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