A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Qi, Jun; Yang, Po; Hanneghan, Martin; Tang, Stephen; Zhou, Bihua

doi:10.1109/jiot.2018.2846359

Cited by 84 publications

(58 citation statements)

References 40 publications

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“…2) Signal Transformation-Key Idea: Requesting users to position their fingers precisely at certain locations is inconvenient and even infeasible in real-world usage scenarios. Instead, an automated signal transformation mechanism is essential in order to rectify the inconsistent signal patterns relative to their positions and map them to the true finger 3 Since the dynamic component F (z) has a starting point and an ending point, we use the average of the two points to represent the sector. gesture.…”

Section: B Signal Transformation: Addressing Inconsistent Pattern Dumentioning

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: B Signal Transformation: Addressing Inconsistent Pattern Dumentioning

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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“…However, it is not suitable for scenarios with long-term dependencies [132]. Examples of IoT use-cases that have employed HMM include anomaly detection [133], physical activity recognition [134], traffic control management [135], health monitoring [136], prediction of user mobility [137], detection of sitting posture activities [138], etc.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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“…Mehrang et al [12] presented an assessment of a human activity monitoring framework that covers some of the principal building blocks required for an activity monitoring system, and is comprised of the best preprocessing and parameter choice for an random forest classifier. Qi et al [13] proposed a two-layer activity recognition framework to classify aerobic, sedentary and free weight activities. However, these frameworks are mainly proposed to monitoring static and dynamic activities, the transitions between multiple activities, which make up a large part of a person's daily activities and can provide additional contextual information for activity recognition, are usually ignored in most current literature due to their short duration.…”

Section: Iirelated Workmentioning

confidence: 99%

Design and assessment of the data analysis process for a wrist-worn smart object to detect atomic activities in the smart home

Cleland

Nugent

et al. 2019

Pervasive and Mobile Computing

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The ability to accurately identify the different activities of daily living (ADLs) is considered as one of the basis to foster new technological solutions inside the smart home. Current ADL recognition proposals, still however, struggle to accurately and robustly identify the range of different activities that can be performed at home, namely static, dynamic and transient activities, and the high variety of technologies and data analysis possibilities to classify the information gathered by the sensors. In this paper, we describe the methodological approach that we have followed for the processing, analysis and classification of data obtained by a simple and non-intrusive smart object with the objective to detect atomic (i.e. non-divisible) activities inside the smart home. The smart object consists of a wrist-worn 3D accelerometer, which presents as its advantages its customizability and usability. We have performed a set of systematic experiments involving ten people and have followed the steps from data gathering to the comparison of different classification techniques, to find out that it is possible to select a complete succession of data processing steps in order to detect, with high accuracy, a set of atomic activities of daily life with the selected smart object, which performs well with different independent datasets besides ours.

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A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors

Abstract: This is a repository copy of A hybrid hierarchical framework for gym physical activity recognition and measurement using wearable sensors.

Cited by 84 publications

References 40 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

Data Analytics in the Internet of Things: A Survey

Design and assessment of the data analysis process for a wrist-worn smart object to detect atomic activities in the smart home

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