Redirigiendo al acceso original de articulo en 21 segundos...
Inicio  /  Applied Sciences  /  Vol: 12 Par: 9 (2022)  /  Artículo
ARTÍCULO
TITULO

An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson?s Disease

Mahdieh Kazemimoghadam and Nicholas P. Fey    

Resumen

Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson?s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson?s disease.

 Artículos similares

       
 
André B. Peres, Mário C. Espada, Fernando J. Santos, Ricardo A. M. Robalo, Amândio A. P. Dias, Jesús Muñoz-Jiménez, Andrei Sancassani, Danilo A. Massini and Dalton M. Pessôa Filho    
This paper presents a comparison of mathematical and cinematic motion analysis regarding the accuracy of the detection of alterations in the patterns of positional sequence during biceps-curl lifting exercise. Two different methods, one with and one with... ver más
Revista: Applied Sciences

 
Yaxin Mao, Lamei Yan, Hongyu Guo, Yujie Hong, Xiaocheng Huang and Youwei Yuan    
Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. I... ver más
Revista: Applied Sciences

 
Hossein Shahverdi, Mohammad Nabati, Parisa Fard Moshiri, Reza Asvadi and Seyed Ali Ghorashi    
Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human?Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representa... ver más
Revista: Information

 
Anne Fischer, Alexandre Beiderwellen Bedrikow, Iris D. Tommelein, Konrad Nübel and Johannes Fottner    
As in manufacturing with its Industry 4.0 transformation, the enormous potential of artificial intelligence (AI) is also being recognized in the construction industry. Specifically, the equipment-intensive construction industry can benefit from using AI.... ver más
Revista: Algorithms

 
Olena Pavliuk, Myroslav Mishchuk and Christine Strauss    
Over the last few years, human activity recognition (HAR) has drawn increasing interest from the scientific community. This attention is mainly attributable to the proliferation of wearable sensors and the expanding role of HAR in such fields as healthca... ver más
Revista: Algorithms