scholarly journals LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4083
Author(s):  
Friedrich Niemann ◽  
Christopher Reining ◽  
Fernando Moya Rueda ◽  
Nilah Ravi Nair ◽  
Janine Anika Steffens ◽  
...  
Keyword(s):  
Motion Capture ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
The Past ◽  
Measurement Units ◽  
The Given ◽  
Semantic Attributes

Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks.

scholarly journals Multimodal Database for Human Activity Recognition and Fall Detection

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1237 ◽  
Author(s):  
Lourdes Martínez-Villaseñor ◽  
Hiram Ponce ◽  
Ricardo Abel Espinosa-Loera
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Fall Detection ◽  
Human Activity Recognition ◽  
Data Sources ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Detection Systems ◽  
Measurement Units

Fall detection can improve the security and safety of older people and alert when fall occurs. Fall detection systems are mainly based on wearable sensors, ambient sensors, and vision. Each method has commonly known advantages and limitations. Multimodal and data fusion approaches present a combination of data sources in order to better describe falls. Publicly available multimodal datasets are needed to allow comparison between systems, algorithms and modal combinations. To address this issue, we present a publicly available dataset for fall detection considering Inertial Measurement Units (IMUs), ambient infrared presence/absence sensors, and an electroencephalogram Helmet. It will allow human activity recognition researchers to do experiments considering different combination of sensors.

Motion capture with inertial measurement units for hand/arm robot teleoperation

2014 ◽  
Vol 45 (1-4) ◽  
pp. 931-937 ◽  
Author(s):  
Futoshi Kobayashi ◽  
Ko Hasegawa ◽  
Hiroyuki Nakamoto ◽  
Fumio Kojima
Keyword(s):  
Motion Capture ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

scholarly journals Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8337
Author(s):  
Hyeokhyen Kwon ◽  
Gregory D. Abowd ◽  
Thomas Plötz
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Large Scale ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
The State ◽  
Model Parameters ◽  
Data Systems ◽  
Performance Improvements ◽  
Measurement Units

Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of activities of interest into virtual IMU data. We demonstrate for the first time how such large-scale virtual IMU datasets can be used to train HAR systems that are substantially more complex than the state-of-the-art. Complexity is thereby represented by the number of model parameters that can be trained robustly. Our models contain components that are dedicated to capture the essentials of IMU data as they are of relevance for activity recognition, which increased the number of trainable parameters by a factor of 1100 compared to state-of-the-art model architectures. We evaluate the new model architecture on the challenging task of analyzing free-weight gym exercises, specifically on classifying 13 dumbbell execises. We have collected around 41 h of virtual IMU data using IMUTube from exercise videos available from YouTube. The proposed model is trained with the large amount of virtual IMU data and calibrated with a mere 36 min of real IMU data. The trained model was evaluated on a real IMU dataset and we demonstrate the substantial performance improvements of 20% absolute F1 score compared to the state-of-the-art convolutional models in HAR.

scholarly journals In Vivo Measurement of Wrist Movements during the Dart-Throwing Motion Using Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5623
Author(s):  
Gabriella Fischer ◽  
Michael Alexander Wirth ◽  
Simone Balocco ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Absolute Difference ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Mean Absolute Difference ◽  
Inertial Measurement ◽  
Dart Throwing ◽  
Measurement Units ◽  
Throwing Motion

Background: This study investigates the dart-throwing motion (DTM) by comparing an inertial measurement unit-based system previously validated for basic motion tasks with an optoelectronic motion capture system. The DTM is interesting as wrist movement during many activities of daily living occur in this movement plane, but the complex movement is difficult to assess clinically. Methods: Ten healthy subjects were recorded while performing the DTM with their right wrist using inertial sensors and skin markers. Maximum range of motion obtained by the different systems and the mean absolute difference were calculated. Results: In the flexion–extension plane, both systems calculated a range of motion of 100° with mean absolute differences of 8°, while in the radial–ulnar deviation plane, a mean absolute difference of 17° and range of motion values of 48° for the optoelectronic system and 59° for the inertial measurement units were found. Conclusions: This study shows the challenge of comparing results of different kinematic motion capture systems for complex movements while also highlighting inertial measurement units as promising for future clinical application in dynamic and coupled wrist movements. Possible sources of error and solutions are discussed.

scholarly journals Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2983
Author(s):  
Marie Sapone ◽  
Pauline Martin ◽  
Khalil Ben Mansour ◽  
Henry Château ◽  
Frédéric Marin
Keyword(s):  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Detection Methods ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
New Methods ◽  
Measurement Units ◽  
Ground Types ◽  
Stride Duration

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

Body Motion Capture and Applications

2021 ◽  
pp. 181-223
Author(s):  
Çağlar Akman ◽  
Tolga Sönmez
Keyword(s):  
Motion Capture ◽  
Situational Awareness ◽  
Body Motion ◽  
Background Information ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Advantages And Disadvantages ◽  
Measurement Units ◽  
Popular Subject ◽  
The Mathematical Model

The motion capture (MoCap) is a highly popular subject with wide applications in different areas such as animations, situational awareness, and healthcare. An overview of MoCap utilizing different sensors and technologies is presented, and the prominent MoCap methods using inertial measurement units and optics are discussed in terms of their advantages and disadvantages. MoCap with wearable inertial measurement units is analyzed and presented specifically with the background information and methods. The chapter puts an emphasis on the mathematical model and artificial intelligence algorithms developed for the MoCap. Both the products from the important technology developers and the proof-of-concept applications conducted by Havelsan are presented within this chapter to involve an industrial perspective. MoCap system will act as a decision support system in either application by providing automatic calculation of metrics or classification, which are the basic tools for decision making.

Motion capture systems for jump analysis

2017 ◽  
Vol 25 (6) ◽  
pp. 890-901 ◽  
Author(s):  
Sendoa Rojas-Lertxundi ◽  
J Ramón Fernández-López ◽  
Sergio Huerta ◽  
Pablo García Bringas
Keyword(s):  
Motion Capture ◽  
State Of The Art ◽  
Vertical Jump ◽  
Electronic Devices ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
New Methods ◽  
Measurement Units ◽  
Professional Systems ◽  
Motion Capture Systems

AbstractThis article presents several methods used in motion capture to measure jumps. The traditional systems to acquire jump information are force plates, but they are very expensive to most people. Amateur sports enthusiasts who want to improve their performance, do not have enough money to spend in professional systems ($+/-20.000$EUR). The price reduction of electronic devices, specifically the inertial measurement units (IMU), are generating new methods of motion capture. In this article we present the state-of the-art motion capture systems for this purpose, from the classical force plates to latest released IMUs. Experiments show that the IMU is equally valid for measuring vertical jump.

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