Accelerometer based human activities and posture recognition

Babu, Arun; Dube, Kudakwashe; Mukhopadhyay, Subhas Chandra; Ghayvat, Hemant; M.V, Jithin Kumar

doi:10.1109/sapience.2016.7684120

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…There are several main methods for posture recognition. One is to use wearable sensors [39], such as wearing accelerometer [2,3,16] and pressure sensor [11]. However, wearing such a device makes subjects feel a sense of burden, which compromises the interactive experience.…”

Section: Introductionmentioning

confidence: 99%

Human posture recognition based on multiple features and rule learning

Ding

Liu

et al. 2020

Int. J. Mach. Learn. & Cyber.

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The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.

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Section: Introductionmentioning

confidence: 99%

Human posture recognition based on multiple features and rule learning

Ding

Liu

et al. 2020

Int. J. Mach. Learn. & Cyber.

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“…The robustness and reliability of HAR with accelerometers is reflected in the variety of the e-health applications where it is found. For example, [36] employs accelerometer-based HAR for posture recognition which helps to monitor falls in elderly people. Such an approach is also present in [37][38][39], which demonstrates the rising popularity of this application.…”

Section: Introductionmentioning

confidence: 99%

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Manjarrés

Narváez

Gasser

et al. 2019

Sensors

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In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.

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Artificial Intelligence and Machine Learning Techniques for Analysis of Yoga Pose

Gupta

Panwar

2023

Lecture Notes in Electrical Engineering

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Accelerometer based human activities and posture recognition

Cited by 5 publications

References 6 publications

Human posture recognition based on multiple features and rule learning

Human posture recognition based on multiple features and rule learning

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Artificial Intelligence and Machine Learning Techniques for Analysis of Yoga Pose

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