IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition

Konak, Orhan; Wegner, Pit; Arnrich, Bert

doi:10.3390/s20247179

Cited by 13 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings ( Figure 2 ) revealed that 54 articles used motion tracking data in health care and physical wellness applications, of which 42 (78%) articles used motion-tracking sensors [ 75 - 81 , 84 - 86 , 88 - 97 , 101 , 103 - 116 , 119 , 121 - 123 , 125 , 126 ]. Overall, 70% (38/54) of the articles used AI technology combined with motion tracking data [ 75 , 77 , 79 , 82 - 85 , 87 , 89 - 91 , 93 , 94 , 97 , 99 - 101 , 103 , 105 , 106 , 108 , 109 , 111 - 113 , 117 - 128 ]. The role of AI mainly functions for data classification, monitoring, and visualization among the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…The studies that involved placing sensors on the full body and in environments were widely reported [ 84 , 94 , 96 , 103 , 109 - 112 , 115 , 116 , 119 , 122 , 123 ], and the purposes of using full-body motion tracking are commonly argued for analyzing specific movements or securing the credibility of system evaluations [ 94 , 106 , 110 , 112 , 115 , 116 , 123 ]. The investigations involving the placement of sensors in physical spaces mostly use motion capture and image processing techniques [ 117 , 119 ].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Wei,

Wang

2024

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Motion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing and intervening in physical activity. Yet, systematic methodologies for evaluating motion tracking data, especially concerning user activity recognition in health care applications, remain underreported. Objective This study aims to systematically review motion tracking in daily living and physical activities, emphasizing the critical interaction among devices, users, and environments from a design perspective, and to analyze the process involved in health care application research. It intends to delineate the design and application intricacies in health care contexts, focusing on enhancing motion tracking data’s accuracy and applicability for health monitoring and intervention strategies. Methods Using a systematic review, this research scrutinized motion tracking data and their application in health care and wellness, examining studies from Scopus, Web of Science, EBSCO, and PubMed databases. The review used actor network theory and data-enabled design to understand the complex interplay between humans, devices, and environments within these applications. Results Out of 1501 initially identified studies, 54 (3.66%) were included for in-depth analysis. These articles predominantly used accelerometer and gyroscope sensors (n=43, 80%) to monitor and analyze motion, demonstrating a strong preference for these technologies in capturing both dynamic and static activities. While incorporating portable devices (n=11, 20%) and multisensor configurations (n=16, 30%), the application of sensors across the body (n=15, 28%) and within physical spaces (n=17, 31%) highlights the diverse applications of motion tracking technologies in health care research. This diversity reflects the application’s alignment with activity types ranging from daily movements to specialized scenarios. The results also reveal a diverse participant pool, including the general public, athletes, and specialized groups, with a focus on healthy individuals (n=31, 57%) and athletes (n=14, 26%). Despite this extensive application range, the focus primarily on laboratory-based studies (n=39, 72%) aimed at professional uses, such as precise activity identification and joint functionality assessment, emphasizes a significant challenge in translating findings from controlled environments to the dynamic conditions of everyday physical activities. Conclusions This study’s comprehensive investigation of motion tracking technology in health care research reveals a significant gap between the methods used for data collection and their practical application in real-world scenarios. It proposes an innovative approach that includes designers in the research process, emphasizing the importance of incorporating data-enabled design framework. This ensures that motion data collection is aligned with the dynamic and varied nature of daily living and physical activities. Such integration is crucial for developing health applications that are accessible, intuitive, and tailored to meet diverse user needs. By leveraging a multidisciplinary approach that combines design, engineering, and health sciences, the research opens new pathways for enhancing the usability and effectiveness of health technologies.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Wei,

Wang

2024

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Recent results have shown that IMUs and machine learning classifiers are very effective in identifying activities in healthy controls [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The accuracies often rely on the number of IMUs used and their location, with the wrist, chest, ankle, and thigh commonly used.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

Bochniewicz

Emmer

Dromerick

et al. 2023

Sensors

View full text Add to dashboard Cite

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3–85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4–72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

show abstract

“…Thanks to technological advances, inertial measurement units (IMUs) are available in small sizes, at low cost, and are widely used for biomedical applications such as gait analysis [ 1 ], rehabilitation [ 2 ], sports [ 3 , 4 , 5 ], injury prevention [ 6 , 7 ], and activity monitoring [ 8 ]. In general, the data from single or multiple IMUs placed on body segments are fused to obtain spatiotemporal parameters [ 9 ] or joint orientation [ 10 ] during movement.…”

Section: Introductionmentioning

confidence: 99%

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

Baniasad

Martin

Crevoisier

et al. 2023

Sensors

View full text Add to dashboard Cite

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations.

show abstract

IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition

Cited by 13 publications

References 27 publications

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Motion Tracking of Daily Living and Physical Activities in Health Care: Systematic Review From Designers’ Perspective

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

Contact Info

Product

Resources

About