Automatically Segmenting Physical Performance Test Items for Older Adults Using a Doppler Radar: A Proof of Concept Study

Zhang, Yiyuan; Babarinde, Oluwatosin J.; Han, Pengxuan; Wang, Xiangyu; Karsmakers, Peter; Schreurs, Dominique; Verschueren, Sabine; Vanrumste, Bart

doi:10.1109/access.2021.3127327

Cited by 3 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… a Accuracy was not reported in 5 papers: [ 27 , 29 , 33 , 34 , 41 ]. b Sensitivity was not reported in 11 papers: [ 22 , 30 , 31 , 33 , 35 - 37 , 40 , 42 , 43 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…In addition, within the AI computation group showing the best results in relation to classification and diagnosis, we found one study [ 32 ] that implemented the C5.0 algorithm AI model, which was used for the analysis of patients’ clinical information sourced from their medical records and self-reported details provided by questionnaires. Other AI models that are frequently implemented [ 26 , 30 , 35 , 41 ] were support vector machine–based models, showing homogeneous findings with high diagnostic accuracy ratios. Another frequently used model was a neural network [ 22 , 23 , 31 , 34 , 44 , 45 ], in which heterogeneity was apparent in relation to the statistics used.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical validity of the tools used (N=26). Accuracy was not reported in 5 papers:[27,29,33,34,41].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Velázquez-Díaz¹,

Arco²,

Ortíz³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. Objective The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). Methods A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. Results We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. Conclusions The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.

show abstract

“… a Accuracy was not reported in 5 papers: [ 27 , 29 , 33 , 34 , 41 ]. b Sensitivity was not reported in 11 papers: [ 22 , 30 , 31 , 33 , 35 - 37 , 40 , 42 , 43 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Velázquez-Díaz¹,

Arco²,

Ortíz³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The experiment was conducted in two places, one staircase for climbing stairs and one indoor room for the other seven items. The detailed information about the experiment can be found in [30]. Participants were asked to wave hands as the flag activity at the start and the end of each item.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Can Wearable Devices and Machine Learning Techniques Be Used for Recognizing and Segmenting Modified Physical Performance Test Items?

Zhang

Wang

Han

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

View full text Add to dashboard Cite

Assessment of physical performance is essential to predict the frailty level of older adults. The modified Physical Performance Test (mPPT) clinically assesses the performance of nine activities: standing balance, chair rising up & down, lifting a book, putting on and taking off a jacket, picking up a coin, turning 360 • , walking, going upstairs, and going downstairs. The activity performing duration is the primary evaluation standard. In this study, wearable devices are leveraged to recognize and predict mPPT items' duration automatically. This potentially allows frequent follow up of physical performance, and facilitates more appropriate interventions. Five devices, including accelerometers and gyroscopes, were attached to the waist, wrists and ankles of eight younger adults. The system was experimented within three aspects: machine learning models, sensor placement, and sampling frequencies, to which the non-causal six-stages temporal convolutional network using 6.25 Hz signals from the left wrist and right ankle obtained the optimal performance. The duration prediction error ranged from 0.63 ± 0.29 s (turning 360 • ) to 8.21 ± 16.41 s (walking). The results suggest the potential for the proposed system in the automatic recognition and segmentation of mPPT items. Future work includes improving the recognition performance of lifting a book and implementing the frailty score prediction.

show abstract

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review (Preprint)

Velázquez-Díaz¹,

Arco²,

Ortíz³

et al. 2023

Preprint

View full text Add to dashboard Cite

BACKGROUND The frailty syndrome is one of the most common non-communicable diseases, and it is associated to lower physical and mental capacities in older adults. The frailty diagnosis is mostly focused on biological variables; however, it is very likely that this diagnosis could fail, due to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. OBJECTIVE To analyse the existing scientific evidence on the use of AI for the diagnosis or detection of the frailty syndrome in older adults, as well as identifying which model provides an enhanced accuracy, sensitivity, specificity, and area under the curve. METHODS A search was conducted using PRISMA protocol on different databases: PubMed, Web of Science, Scopus and Google Scholar. The search strategy was conducted following PICO criteria. The studies selected met the defined inclusion and exclusion criteria, and those contained information on diagnosis or detection of the frailty syndrome in older adults through any type of AI. RESULTS A total of 926 studies were identified from the 4 databases. After pre-processing and screening, only 26 studies were included in this review. All studies were aimed at diagnosing and detecting frailty syndrome. Machine learning was the most widely used type of AI, included in 18 studies. Moreover, of the 26 included studies, 9 studies used clinical data, being the clinical histories the most used type of data in this section. The remaining 17 studies used non-clinical data, with activity monitoring using an inertial sensor in a clinical and non-clinical context were the most used data in this section. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity or area under the curve ≥90. CONCLUSIONS The findings of this exploratory review reports the overall status of recent studies using AI to diagnose frailty syndrome. Moreover, findings show that the combined use through AI of clinical data, along with non-clinical information, such as the kinematics of inertial sensors that monitor activities in a non-clinical context, could be a correct tool for the diagnosis of frailty syndrome.

show abstract

Automatically Segmenting Physical Performance Test Items for Older Adults Using a Doppler Radar: A Proof of Concept Study

Cited by 3 publications

References 34 publications

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Can Wearable Devices and Machine Learning Techniques Be Used for Recognizing and Segmenting Modified Physical Performance Test Items?

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review (Preprint)

Contact Info

Product

Resources

About