Human daily activity recognition by fusing accelerometer and multi-lead ECG data

Jia, Ruiting; Liu, Bin

doi:10.1109/icspcc.2013.6664056

Cited by 22 publications

(13 citation statements)

References 10 publications

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“…First, RI of all features were calculated, and those with RI less than zero were eliminated. Then, the sequential forward selection (SFFS) algorithm was carried out to reduce the dimension of the retained feature sets [44,45]. In particular, the evaluation criterion of the SFFS algorithm was the classification performance of selected feature subsets with the random forests (RFS) classifier.…”

Section: Feature Selectionmentioning

confidence: 99%

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology

Chen

Sun

2021

Diagnostics

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Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects’ health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

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Section: Feature Selectionmentioning

confidence: 99%

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology

Chen

Sun

2021

Diagnostics

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“…However, their proposed approach was only utilized on two similar kind of datasets to detect human activities. In [24], Jia et al offered a novel scheme for detecting daily human living activities by fusing IMU and ECG data. They extracted both time and frequency features from raw sensor data.…”

Section: B Dld Via Fused Sensorsmentioning

confidence: 99%

MS-DLD: Multi-Sensors Based Daily Locomotion Detection via Kinematic-Static Energy and Body-Specific HMMs

et al. 2022

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More adaptable and user-independent techniques are required for multi-sensors based daily locomotion detection (MS-DLD). This research study proposes a couple of locomotion detection methods using body-worn multi-sensors to successfully categorize several locomotion transitions, including falling, walking, jogging, and jumping, along with bodyspecific sensors based on the modified hidden Markov models (HMMs) approach. This research presents both standard and state-of-the-art methods for MS-DLD. Conventionally, to improve MS-DLD process, the proposed methodology consists of a wavelet transformed Quaternion-based filter for the inertial signals, patterns recognition in the form of kinematic-static energies, and state-of-the-art multi-features extraction. These features include entropy, spectral, and cepstral coefficients domains. Then, fuzzy logic-based optimization has been introduced in order to achieve the selective features by converting them into codewords. This paper also introduces another state-of-the-art way to model daily locomotion detection and derives body-specific modified HMMs. The model divides the sensor data into three active body-specific parts including head sensors, mid-body sensors, and lower body sensors. Body-specific modified HMMs have been provided with raw data for the three active body-specific sensors and gave better results with less computational complexities when compared to the conventional methods. The proposed systems have been experimentally assessed and trialed over three diverse publicly available datasets: the UP-Fall dataset consisting of falling and other daily life activities, the IM-WSHA dataset comprising everyday locomotion actions, and the ENABL3S gait and locomotion dataset consisting of multiple gait movements. Experimental outcomes indicate that the proposed conventional technique achieved improved results and outperformed existing systems based on detection accuracies of 90.0% and 87.5% over UP-Fall, 86.0% and 88.3% over IM-WSHA, and 86.7% and 90.0% over ENABL3S datasets for kinematic and static energy patterns, respectively. Further, the results show that the state-of-the-art body-specific modified HMMs method achieved 94.3% and 95.0% over UP-Fall, 92.0% and 93.3% over IM-WSHA, and 90.0% and 95.0% over ENABL3S datasets for kinematic and static patterned signals, respectively. The results of state-of-theart efficient system show a significant increase in detection accuracy when compared to standard systems.

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“…With up to twelve electrodes, ECG signals are commonly used to check different heart conditions. The ECG signal is also a popular explored signal for HAR and commonly combined with other inertial sensors [97,98]. Since the cells in the brain communicate through fast electrical impulses, researchers developed EEG equipment to record the brain's electrical activity by using small metal electrodes attached to the scalp [99].…”

Section: Physiological Sensingmentioning

confidence: 99%

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Bian

Liu

Zhou

et al. 2022

Sensors

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Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

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Human daily activity recognition by fusing accelerometer and multi-lead ECG data

Cited by 22 publications

References 10 publications

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology

MS-DLD: Multi-Sensors Based Daily Locomotion Detection via Kinematic-Static Energy and Body-Specific HMMs

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

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