Robust activity recognition using wearable IMU sensors

Prathivadi, Yashaswini; Wu, Jian; Bennett, Terrell R.; Jafari, Roozbeh

doi:10.1109/icsens.2014.6985041

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…The motor activity (MA) of the user is a relevant parameter that provides useful information to the platform. For example, it allows to evaluate the health and wellness in users with neurodegenerative disorders that influence in the motor functionality [16]. This parameter allows to quantify the arm movements and provides data about the user's displacement.…”

Section: Motor Activity Sensormentioning

confidence: 99%

Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders

et al. 2021

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The percentage of people around the world who are living with some kind of disability or disorder has increased in recent years and continues to rise due to the aging of the population and the increase in chronic health disorders. People with disabilities find problems in performing some of the activities of daily life, such as working, attending school, or participating in social and recreational events. Neurological disorders such as epilepsy, learning disabilities, autism spectrum disorder, or Alzheimer’s, are among the main diseases that affect a large number of this population. However, thanks to the assistive technologies (AT), these people can improve their performance in some of the obstacles presented by their disorders. This paper presents a new system that aims to help people with neurological disorders providing useful information about their pathologies. This novelty system consists of a platform where the physiological and environmental data acquisition, the feature engineering, and the machine learning algorithms are combined to generate customs predictive models that help the user. Finally, to demonstrate the use of the system and the working methodology employed in the platform, a simple example case is presented. This example case carries out an experimentation that presents a user without neurological problems that shows the versatility of the platform and validates that it is possible to get useful information that can feed an intelligent algorithm.

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Section: Motor Activity Sensormentioning

confidence: 99%

Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders

et al. 2021

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“…The IMU sensor provides the acceleration, angular velocity, and magnetometer values, which are analyzed to recognize human activities like walking, running, and even falling down [13]. Prathivadi et al's team did the research and proved that IMU has good performance on activity recognition [14].…”

Section: Related Workmentioning

confidence: 99%

LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection

Han

Song

Gozho

et al. 2020

Wireless Communications and Mobile Computing

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Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.

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“…Wearable sensors, including inertial measurement units (IMUs), provide a portable means of accessing user motion outside the confines of a lab [11,12]. IMUs are widely used to estimate joint, segment, and muscle kinematics and kinetics from accelerations, angular Sensors 2024, 24, 3657 2 of 15 velocities, and local magnetic fields [10,13]. Although IMUs offer a popular and modular solution to HMIP, excessive computational complexity due to additional sensors may delay intent prediction.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements

Hollinger,

Schall,

Chen

et al. 2024

Sensors

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The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.

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Robust activity recognition using wearable IMU sensors

Cited by 8 publications

References 10 publications

Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders

Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders

LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection

The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements

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