Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients

Ziegl, Andreas; Hayn, Dieter; Kästner, Peter; Löffler, Kerstin; Weidinger, Lisa; Brix, Bianca; Goswami, Nandu; Schreier, Günter

doi:10.1109/embc44109.2020.9176574

Cited by 3 publications

(3 citation statements)

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“…Further, uTUG implemented a hybrid approach to automatically detect TUG tests, using heuristic and statistical parameters in addition to machine learning prediction. uTUG, Dubois et al [25], Ziegl et al [35], Hellmers et al [34] and Roshdibenam et al [15] utilized a combination of statistical or rule-based and machine learning processing after features extraction.…”

Section: Discussionmentioning

confidence: 99%

“…Ziegl et al [35] investigated the TUG's potential for fall prediction by conducting a clinical study with 31 elderly participants living in nursing homes. The authors examined the differences between the use and non-use of a walking aid.…”

Section: Related Workmentioning

confidence: 99%

“…After the peaks detection, the algorithm searches sequentially for initial and final resting markers (InitialRest and FinalRest intervals, respectively, on Table 3). The resting period is considered an additional important empirical marker of the TUG test [35]. The initial resting segment is represented by the InitialRest interval and the final resting period is represented by FinalRest interval in Fig.…”

Section: Prominent Peaks Detectionmentioning

confidence: 99%

See 2 more Smart Citations

uTUG: An unsupervised Timed Up and Go test for Parkinson’s disease

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Prominent Peaks Detectionmentioning

confidence: 99%

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uTUG: An unsupervised Timed Up and Go test for Parkinson’s disease

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Roth

et al. 2023

Biomedical Signal Processing and Control

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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

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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.

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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

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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.

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Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients

Cited by 3 publications

References 9 publications

uTUG: An unsupervised Timed Up and Go test for Parkinson’s disease

uTUG: An unsupervised Timed Up and Go test for Parkinson’s disease

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 (Preprint)

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