Improved extended Kalman fusion method for upper limb motion estimation with inertial sensors

Zhang, Sheng; Kang, Xiao; Zhang, Qian; Zhang, Hao; Liu, Yi

doi:10.1109/icicip.2013.6568143

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…As a matter of fact, previous studies find the applicability of systems with equal or even lower frequency. These studies are targeted to motion estimation, but down sample inertial sensor data from commercial or custom IMUs at a final frequency of 50 Hz [ 43 , 63 ], use the system for rehabilitation supervision at a sampling rate of 40 Hz [ 36 ], or perform ambulatory human joint motion monitoring at 50 Hz [ 64 ]. Other limitations of the system regarding the kinematic model have been commented in Section 2.4 .…”

Section: Discussionmentioning

confidence: 99%

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

González-Alonso

Oviedo-Pastor

Hernández

et al. 2021

Sensors

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Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.

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Section: Discussionmentioning

confidence: 99%

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

González-Alonso

Oviedo-Pastor

Hernández

et al. 2021

Sensors

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“…Recently, various research studies have been done to analyze human actions based on wearable sensors [9]. A large number of these studies focus on identifying which are the most informative features that can be extracted from the actions data as well as in searching which are the most effective machine learning algorithms for classifying these actions [10]. Wearable sensors attached to human anatomical references, e.g., inertial and magnetic sensors (accelerometers, gyroscopes and magnetometers), vital sign processing devices (heart rate, temperature) and RFID tags, can be used to gather information about the behavioral patterns of a person.…”

Section: Introductionmentioning

confidence: 99%

“…Another line of research is to find the most appropriate computational model to represent human action data. However, the robustness to model parameters of many existing human action recognition techniques are still quite limited [10]. In addition, the feature sets used for the classification do not describe how the actions were performed in terms of human motion.…”

Section: Introductionmentioning

confidence: 99%

High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

López-Nava

Muñoz-Meléndez

2018

UCAmI 2018

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Action recognition is important for various applications, such as, ambient intelligence, smart devices, and healthcare. Automatic recognition of human actions in daily living environments, mainly using wearable sensors, is still an open research problem of the field of pervasive computing. This research focuses on extracting a set of features related to human motion, in particular the motion of the upper and lower limbs, in order to recognize actions in daily living environments, using time-series of joint orientation. Ten actions were performed by five test subjects in their homes: cooking, doing housework, eating, grooming, mouth care, ascending stairs, descending stairs, sitting, standing, and walking. The joint angles of the right upper limb and the left lower limb were estimated using information from five wearable inertial sensors placed on the back, right upper arm, right forearm, left thigh and left leg. The set features were used to build classifiers using three inference algorithms: Naive Bayes, K-Nearest Neighbours, and AdaBoost. The F- m e a s u r e average of classifying the ten actions of the three classifiers built by using the proposed set of features was 0.806 ( σ = 0.163).

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Upper Body Motion Tracking System with Inertial Sensors for Ergonomic Issues in Industrial Environments

Caputo

D’Amato

Spada³

et al. 2016

Advances in Intelligent Systems and Computing

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Improved extended Kalman fusion method for upper limb motion estimation with inertial sensors

Cited by 4 publications

References 11 publications

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

High-Level Features for Recognizing Human Actions in Daily Living Environments Using Wearable Sensors

Upper Body Motion Tracking System with Inertial Sensors for Ergonomic Issues in Industrial Environments

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