Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity Recognition

Lü, Wei; Fan, Fugui; Chu, Jinghui; Jing, Peiguang; Su, Yuting

doi:10.1109/jiot.2018.2873594

Cited by 67 publications

(23 citation statements)

References 44 publications

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“…Experiment 2: Verify that the dynamic component of CSI ratio rotates with respect to the static component. We move the metal plate from 105cm to 114.4cm away from 2 The distance is measured from the metal plate to the LoS path of the transceiver pair. The results are shown in Figure 10 and we can see that the dynamic component generates close to perfect circles, demonstrating the correctness of the property.…”

Section: A Csi Ratio: a Better Base Signalmentioning

confidence: 99%

“…Internet of Things (IoT) technologies have attracted significant attention in recent years, playing an important role in the development of various applications, such as activity and gesture recognition [1], [2], [3] and human-computer interaction. To support these applications, the capabilities of contactless sensing have been explored in various smart IoT devices, such Kai Copyright (c) 2019 IEEE.…”

Section: Introductionmentioning

confidence: 99%

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WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Niu

Zhang

Jiang

et al. 2019

IEEE Internet Things J.

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Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with Motor Neurone Disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voicebased solutions fail. In contrast, most patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus we propose to develop a Morse code based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the Channel State Information (CSI) between two antennas to enhance the Signal to Noise Ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity.

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Section: A Csi Ratio: a Better Base Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Niu

Zhang

Jiang

et al. 2019

IEEE Internet Things J.

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show abstract

“…[19] [20] recognizes daily activities by using wearable sensors and additionally environmental sound with DNN (Deep Neural Network). [21] uses S transform and Gaussian windows to extract features of the activities but its success related with length of the activity which is very dataset specific. Many studies uses kNN algorithm to classify activities on.…”

Section: Introductionmentioning

confidence: 99%

Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

et al. 2020

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In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new stateof-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly.

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“…Vision based process have many complications when applied practically. On the other hand sensor based methods are easy to implement and robust by using IoT devices [1,2]. Human activities detected using sensor incorporates many activities (sitting, standing, walking, running, walking upstairs, walking downstairs).…”

Section: Introductionmentioning

confidence: 99%

An Electric Eye for Human Activity Recognition: A Hybrid Neural Network

Prethi¹,

Sangeetha²,

Nithya³

et al. 2020

IJEAT

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A real time detection of human movements is a practical solution to monitor aged people or mentally challenged people with the permission of their family. Household person is needed to monitor the elder and differently abled people. Instead of monitoring their activities with the help of other people, smart phones are used as a remote to monitor their activities and simultaneously send the message to their family members. The accelerometer sensor placed in the mobile phones. It is used to identify the activities of the person who holds the mobile phones. The most commonly used classifier technique is Naive Bayes classifier which has a limitation of handle with the large set of data. To overcome this defect, the proposed system classifies the data using k-nearest neighbor (K-NN) technique and Neuroevolution. This system recognize some representative human movements such as walking, climbing upstairs, climbing downstairs, standing, sitting and running ,using a conventional mobile equipped with a single tri-axial accelerometer sensor.

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Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity Recognition

Cited by 67 publications

References 44 publications

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals

Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

An Electric Eye for Human Activity Recognition: A Hybrid Neural Network

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