scholarly journals Development of Calibration, Stride Tracking, and Activity Recognition Algorithms using Inertial Measurement Units toward Long Term Out-of-Lab Gait Measurements

2014 ◽  
Author(s):  
Travis Simpson
Keyword(s):  
Activity Recognition ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Recognition Algorithms ◽  
Measurement Units

A novel acquisition platform for long-term breathing frequency monitoring based on inertial measurement units

2020 ◽  
Vol 58 (4) ◽  
pp. 785-804 ◽  
Author(s):  
Ambra Cesareo ◽  
Emilia Biffi ◽  
David Cuesta-Frau ◽  
Maria G. D’Angelo ◽  
Andrea Aliverti
Keyword(s):  
Breathing Frequency ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Frequency Monitoring ◽  
Measurement Units

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 Activity Recognition Using Multiple Inertial Measurement Units

IRBM ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 180-186 ◽  
Author(s):  
N. Jalloul ◽  
F. Porée ◽  
G. Viardot ◽  
P. L'Hostis ◽  
G. Carrault
Keyword(s):  
Activity Recognition ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

scholarly journals Foot-Mounted Inertial Measurement Units-Based Device for Ankle Rehabilitation

2018 ◽  
Vol 8 (11) ◽  
pp. 2032 ◽  
Author(s):  
Alfonso Gómez-Espinosa ◽  
Nancy Espinosa-Castillo ◽  
Benjamín Valdés-Aguirre
Keyword(s):  
Sports Injuries ◽  
Angular Position ◽  
Ankle Sprains ◽  
Inertial Measurement Units ◽  
Active Therapy ◽  
Inertial Measurement ◽  
Ankle Rehabilitation ◽  
Measurement Units ◽  
And Inversion

Ankle sprains are frequent injuries that occur among people of all ages. Ankle sprains constitute approximately 15% of all sports injuries, and are the most common traumatic emergencies. Without proper treatment and rehabilitation, a more severe sprain can weaken the ankle, making it more likely for new injures, and leading to long-term problems. In this work, we present an inertial measurement units (IMU)-based physical interface for measuring the foot attitude, and a graphical user interface that acts as a visual guide for patient rehabilitation. A foot-mounted physical interface for ankle rehabilitation was developed. The physical interface is connected to the computer by a Bluetooth link, and provides feedback to the patient while performing dorsiflexion, plantarflexion, eversion, and inversion exercises. The system allows for in-home rehabilitation at an affordable price while engaging the patient through active therapy. According to the results, more consistent rehabilitation could be achieved by providing feedback on foot angular position during therapy procedures.

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.

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