Pain Level Detection From Facial Image Captured by Smartphone

Hasan, Kamrul; Ahsan, Golam Mushih Tanimul; Ahamed, Sheikh Iqbal; Love, Rechard; Salehi, Reza

doi:10.2197/ipsjjip.24.598

Cited by 16 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combining movement quality with pain level evaluation-Patients' pain level during a rehabilitation exercise session can reflect their health status, and thus, it can be an important indicator of the treatment outcome. Accordingly, a great deal of study focused on assessing pain level [109], [110], [112], [165], [166]. E.g., in [110] facial images captured by a smartphone were used to estimate the pain level of cancer patients.…”

Section: Future Directionsmentioning

confidence: 99%

“…Accordingly, a great deal of study focused on assessing pain level [109], [110], [112], [165], [166]. E.g., in [110] facial images captured by a smartphone were used to estimate the pain level of cancer patients. Milton et al [165] studied the relation between common symptoms and health aspects, and found that the pain intensity produces stronger relationships when compared to other symptoms.…”

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A review of computational approaches for evaluation of rehabilitation exercises

Liao

Vakanski

Xian

et al. 2020

Computers in Biology and Medicine

View full text Add to dashboard Cite

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.

show abstract

Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

A review of computational approaches for evaluation of rehabilitation exercises

Liao

Vakanski

Xian

et al. 2020

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…The main challenge in studies related to pain is the establishment of a ground truth with reliable labels. A novel approach to collect data was suggested in the study of Hasan et al [82], where they collected images and self-reported levels of pain by patients using a mobile app. While the use of mobile apps could ease the data collection process, special attention should be given to the reliability of data and ethical concerns which might raise from them.…”

Section: Table I Homology Of Pain Expression Across the Lifespanmentioning

confidence: 99%

A Survey on Computer Vision for Assistive Medical Diagnosis From Faces

Thevenot

López

Hadid

2018

IEEE J. Biomed. Health Inform.

133

View full text Add to dashboard Cite

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives, such as the need for interdisciplinary collaboration and collecting publicly available databases, are highlighted.

show abstract

“…Predicted pain level range was also the same as input pain level 0-10 where 0 means no pain and 10 means the highest level of pain. Then the range of pain level (0-10) was transformed into three (3) categorical levels: low (0-3), mid (4-6) and high (7)(8)(9)(10). This classification into categories is similar to the Brief Pain Inventory which has been proposed and validated across different cultures [29].…”

Section: Cross-sectional Evaluationmentioning

confidence: 99%

“…Assessment of pain is an important step towards the treatment of pain. There is a need for manageable, valid and reliable tools to assess pain [10]. Accurate assessment of pain is critical for the identification of appropriate interventions and for evaluating the effectiveness of such interventions in the clinical setting [11].…”

Section: Introductionmentioning

confidence: 99%