Human activity recognition using inertial measurement units and smart shoes

Chinimilli, Prudhvi Tej; Redkar, Sangram; Zhang, Wenlong

doi:10.23919/acc.2017.7963159

Cited by 26 publications

(7 citation statements)

References 17 publications

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“…Specific patterns can also be identified for binary control. 66,67 For instance, Chinimilli et al 68 proposed an intelligent fuzzy inference algorithm using smart shoes and inertial measurement units (IMUs) to recognize human activities including sitting, standing, walking, going upstairs, going downstairs and jogging with an overall accuracy higher than 95%. However, discontinuous control variables caused by the switching of discrete control modes cannot be avoided.…”

Section: Intent Definitionmentioning

confidence: 99%

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

Yang

Shi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The exoskeleton has emerged as a promising technology to enhance humans’ strength and boost users’ efficiency. In order to provide congruent human–machine interaction for assisting the users, exoskeletons should have knowledge of human’s planned action and accordingly command the robot by the designed controller. It means the ubiquitous aspect of exoskeletons lies on motion intent understanding and active compliance control. In the last decade, extensive research has been conducted on the two topics. However, no major breakthrough has been made. Thus, a systematic review and analysis on this very subject is of great significance in developing exoskeletons. Within this context, this review first surveys the history of lower limb exoskeletons to summarize the various technologies for realizing transparent human exoskeleton coordination. Then, an overview about motion intent understanding and compliance control strategies are presented in detail. Furthermore, the future trend and research directions are also outlined.

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Section: Intent Definitionmentioning

confidence: 99%

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

Yang

Shi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…On the other hand, the air pressure measurement system is bulky, requires inordinate amounts of power, and therefore is not easily wearable. A more recent example that also uses air pressure sensors for GRF measurement is [26], where the target application is human activity classification. In addition to air pressure sensors, this system uses four inertial motion units to assist with activity identification by measuring both GRF and knee joint angles.…”

Section: Review Of Prior Artmentioning

confidence: 99%

Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring

Muzaffar

Elfadel

2020

Sensors

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Traditional pedobarography methods use direct force sensor placement in the shoe insole to record pressure patterns. One problem with such methods is that they tap only a few points on the flat sole under the foot and, therefore, do not account for the total ground reaction force. As a result, body weight tends to be under-estimated. This disadvantage has made it more difficult for pedobarography to be used to monitor many diseases, especially when their symptoms include body weight changes. In this paper, the problem of pedobarographic body weight measurement is addressed using a novel ergonomic shoe-integrated sensor array architecture based on concentrating the applied force via three-layered structures that we call Sandwiched Sensor Force Consolidators (SSFC). A shoe prototype is designed with the proposed sensors and shown to accurately measure body weight with an achievable relative accuracy greater than 99%, even in the presence of motion. The achieved relative accuracy is at least 4X better than the existing state of the art. The SSFC shoe prototype is built using readily available soccer shoes and piezoresistive FlexiForce sensors. To improve the wearability and comfort of the instrumented shoe, a semi-computational sensor design methodology is developed based on an equivalent-area concept that can accurately account for SSFC’s with arbitrary shapes. The search space of the optimal SSFC design is shown to be combinatorial, and a high-performance computing (HPC) framework based on OpenMP parallel programming is proposed to accelerate the design optimization process. An optimal sensor design speedup of up to 22X is shown to be achievable using the HPC implementation.

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“…Researchers seek to couple well-known models of IMU sensor dynamics with novel human kinematic models in order to fuse human motion capture measures of interest from the body-worn IMU data. To date, IMUs have found use in many human motion applications, including clinical sciences [ 1 , 2 , 11 , 12 , 13 ], sports performance [ 14 , 15 , 16 , 17 , 18 ], activity recognition [ 19 , 20 , 21 , 22 , 23 ], occupational ergonomics [ 3 ], and even spacesuit fit evaluation [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking

McGrath

Stirling

2022

Sensors

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Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated (N=12) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87∘ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70∘ relative RMSE), and hip abduction/adduction angle (4.56∘ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27∘ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking).

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Human activity recognition using inertial measurement units and smart shoes

Cited by 26 publications

References 17 publications

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring

Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking

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