Multimodal Database for Human Activity Recognition and Fall Detection

Martínez-Villaseñor, Lourdes; Ponce, Hiram; Espinosa-Loera, Ricardo Abel

doi:10.3390/proceedings2191237

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Human motion recognition (HMR) is a technology domain that recognizes and distinguishes different types of human activities using sensor data [ 1 ]; it is widely used in rehabilitation and medical treatment like the classification and rehabilitation evaluation of patients with hip osteoarthritis, neurological disorders such as stroke, and Parkinson’s disease through gait analysis [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. It also has been used in training assistance like exercise coaching through motion tracking and feedback, speed and position tracking in sports training [ 12 , 13 , 14 , 15 , 16 , 17 ], sudden fall prevention [ 18 ] along with the development of wearable sensor technology. This paper describes the development of a foot–ground contact phase classification (FGCC) algorithm as FGCC is one of the most fundamental and elemental processes in lower-limb motion analysis.…”

Section: Introductionmentioning

confidence: 99%

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Jeon

Kim

et al. 2020

Sensors

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Classification of foot–ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, and exoskeleton motion capture. In this study, sliding-window label overlapping of time-series wearable motion data in training dataset acquisition is proposed to accurately detect foot–ground contact phases, which are composed of 3 sub-phases as well as the swing phase, at a frequency of 100 Hz with a convolutional neural network (CNN) architecture. We not only succeeded in developing a real-time CNN model for learning and obtaining a test accuracy of 99.8% or higher, but also confirmed that its validation accuracy was close to 85%.

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Section: Introductionmentioning

confidence: 99%

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Jeon

Kim

et al. 2020

Sensors

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“…After obtaining the formula for relative acceleration, the next step is to determine the change in angle during the falling motion. This requires a trigonometric formula, shown in (2).…”

Section: Methodsmentioning

confidence: 99%

“…The development of technology in the field of electronics has numerous benefits for daily life, including making work easier, providing comfort, and reducing the risk of work accidents [1], [2]. One area where technology can be particularly useful is in the realm of fall detection and prevention, particularly for elderly individuals who may be more prone to falls due to the decline in function of certain organs [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Design and build an airbag system for elderly fall protection using the MPU6050 sensor module

Suprapto,

Kusuma,

Firdaus

et al. 2024

IJRES

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The use of technology has a significant impact to reduce the consequences of accidents. Sensors, small components that detect interactions experienced by various components, play a crucial role in this regard. This study focuses on how the MPU6050 sensor module can be used to detect the movement of people who are falling, defined as the inability of the lower body, including the hips and feet, to support the body effectively. An airbag system is proposed to reduce the impact of a fall. The data processing method in this study involves the use of a threshold value to identify falling motion. The results of the study have identified a threshold value for falling motion, including an acceleration relative (AR) value of less than or equal to 0.38 g, an angle slope of more than or equal to 40 degrees, and an angular velocity of more than or equal to 30 °/s. The airbag system is designed to inflate faster than the time of impact, with a gas flow rate of 0.04876 m3 /s and an inflating time of 0.05 s. The overall system has a specificity value of 100%, a sensitivity of 85%, and an accuracy of 94%.

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“…Section 4 identifies currently implemented HAR solutions in the literature that has been summarized (Table 5). These solutions are related to the following applications, such as active and assisted living (AAL) [103,118], fall detection (FD) [7,10,26,91,119,120], automatic estimation of activity capability for rheumatic and musculoskeletal disease (RMD) [121], monitoring of elderly people [38,75] and ambulatory monitoring (AM) [7,80]. Solutions combine different HAR techniques in diverse HAR stages, depending on their research goal.…”

Section: The Optimization Of Energy Consumption and Latency In Harmentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

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Multimodal Database for Human Activity Recognition and Fall Detection

Cited by 10 publications

References 21 publications

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Fast Wearable Sensor–Based Foot–Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping

Design and build an airbag system for elderly fall protection using the MPU6050 sensor module

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

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