HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map

Htet, Ye; Zin, Thi Thi; Tin, Pyke; Tamura, H.; Kondo, Kazuhiro; Chosa, Etsuo

doi:10.3390/ijerph191912055

Cited by 10 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A smartphone-based system [43] has also been developed for transition recognition. In previous research, transition-aware elderly action recognition systems were developed using a combination of the Hidden Markov Model (HMM) with ML algorithms and temporally dependent features extracted from body movements [10], [11]. However, both approaches face challenges such as confusion between transition states and specific actions, leading to unsatisfactory results.…”

Section: Transition-aware Action Recognitionsmentioning

confidence: 99%

“…Each frame was structured as an image with a resolution of 320×180 pixels, capturing the data at a frame rate of 5fps. For enhanced visualization and subsequent analysis, the retrieved depth images were colorized using the hue space colorization method, a process interpreted in a previous study [10]. Fig.…”

Section: B Understanding the Datamentioning

confidence: 99%

“…Previously, action recognition models [9], [10], [11] were developed for elderly activity monitoring using various feature extraction methods and ML-based recognition approaches. While these models achieved reliable performance for specific actions, such as sitting, standing, and lying down, accurately recognizing transition states proved challenging.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

Htet,

Zin,

Tin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The global rise in the elderly population, which presents challenges to healthcare systems owing to labor shortages in caregiving facilities, necessitates innovative solutions for elderly care services. Smart aging technologies such as robotic companions and digital home gadgets, offer a solution to these challenges by improving the elderly's quality of life and assisting caregivers. However, limitations in data privacy, real-time processing, and reliability often hinder the effectiveness of the existing technologies. Among these, privacy concerns are a major barrier to ensuring user trust and ethical implementation. Therefore, this study proposes a more effective approach for smart aging through elderly activity monitoring that prioritizes data privacy. The proposed system utilizes stereo depth cameras to monitor the activities of the elderly. Data were collected from real-world environments with the participation of six elderly individuals from a care center and hospital. This system focuses on recognizing common daily actions of the elderly including sitting, standing, lying down, and seated in a wheelchair. Additionally, it recognizes transition states (in-between actions such as changing from sitting to standing) that are crucial for assessing balance issues. By integrating motion information with a deep-learning architecture, the system achieved a high accuracy of 99.42% in recognizing daily actions in real-time. This high accuracy was maintained even with minimal data from new environments through transfer learning, and the adaptability of this model ensured its potential for real-world applications. For intuitive interaction between the caregivers and the system, a user-friendly graphical interface (GUI) was also designed in the proposed approach.

show abstract

Section: Transition-aware Action Recognitionsmentioning

confidence: 99%

Section: B Understanding the Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

Htet,

Zin,

Tin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…These techniques can be used for various tasks such as speech recognition, speaker identification, and emotion recognition. An unknown sample can be predicted by training a neural network with feature vectors, while classification and recognition can be performed using techniques such as Support Vector Machine (SVM), the Hidden Markov Model (HMM), Dynamic Time Warping (DTW), and Vector Quantization (VQ) [ 21 , 22 , 23 , 24 , 25 ].…”

Section: Voice Pain and Artificial Intelligencementioning

confidence: 99%

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

et al. 2023

View full text Add to dashboard Cite

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

show abstract

“…Hidden Markov model (HMM) was created by Rabiner and Juang after being introduced by Rabiner and Juang [15]. It is employed for classification, prediction, comparison, and speech and pattern recognition [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Integration of Ontology Transformation into Hidden Markov Model

Warda¹,

Kaladzavi²,

Samdalle³

et al. 2022

IDA

View full text Add to dashboard Cite

The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respectively. Knowledge is represented in many different fields using the central element of the Semantic Web dubbed ontology. The authors employed machine learning technologies like HMM to add knowledge to these ontologies or to extract knowledge from within them. The meaning obtained from ontologies is not described during this task. The ontology triples that were extracted using SPARQL queries are used in this paper to transform the ontology into an HMM in order to handle this semantic. The Pizza ontology has been used to implement this method, which is based on lightweight ontologies.

show abstract

HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map

Cited by 10 publications

References 39 publications

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

Integration of Ontology Transformation into Hidden Markov Model

Contact Info

Product

Resources

About