Sensor-Based Human Activity and Behavior Research: Where Advanced Sensing and Recognition Technologies Meet

Liu, Hui; Gamboa, Hugo; Schultz, Tanja

doi:10.3390/s23010125

Cited by 30 publications

(20 citation statements)

References 19 publications

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“…Conventional sensors that are appropriate for HAR and Human behaviour recognition (HBR) have been sufficiently researched and validated. These include optical sensors like cameras for recording video data, bioelectrical, bio magnetic, and biomechanical sensors for wearable applications, and external sensors for smart homes [1]. New applications made possible by sensor-based HAR/HBR are beginning to take shape, including single motion duration analysis [2], interactive education [1], high-level feature design [3], time series information retrieval [4] and detection of falls [5].…”

Section: Introductionmentioning

confidence: 99%

“…These include optical sensors like cameras for recording video data, bioelectrical, bio magnetic, and biomechanical sensors for wearable applications, and external sensors for smart homes [1]. New applications made possible by sensor-based HAR/HBR are beginning to take shape, including single motion duration analysis [2], interactive education [1], high-level feature design [3], time series information retrieval [4] and detection of falls [5]. They are still being thoroughly researched for more effective and efficient use.…”

Section: Introductionmentioning

confidence: 99%

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Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

Verma,

Tyagi,

Aneja

2024

Eng. Res. Express

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In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual's body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data. 

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

Verma,

Tyagi,

Aneja

2024

Eng. Res. Express

View full text Add to dashboard Cite

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“…Furthermore, our approach enables a systematic evaluation of the unimodal and multimodal models, assessing their suitability and informativeness of each modality in decoding mental effort. This knowledge provides researchers in the fields of Human Activity Recognition and Behaviour Recognition with references for selecting suitable sensors, as well as a validated multimodal experimental design and ML processing pipeline [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Gado

Lingelbach

Wirzberger

et al. 2023

Sensors

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Humans’ performance varies due to the mental resources that are available to successfully pursue a task. To monitor users’ current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

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“…Therefore, it is challenging to intuitively predict the motion intention of the upper limb while simultaneously driving its multi-DOF joints. In the state of the art, two main approaches of motion intention detection are explored: human-motion activity recognition [ 7 ] and continuously decoding joint angles in time series [ 8 ]. However, the methods covered above could not accurately and intuitively recognize the intended reaching goal of humans’ upper limbs in planar space and thus cannot be used to provide assistance in reaching a specific goal.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang

Garg

Gao

et al. 2023

Sensors

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The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.

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Sensor-Based Human Activity and Behavior Research: Where Advanced Sensing and Recognition Technologies Meet

Abstract: Human activity recognition (HAR) and human behavior recognition (HBR) have been playing increasingly important roles in the digital age [...]

Cited by 30 publications

References 19 publications

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

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