Validity of digital Trail Making Test and Bells Test in elderlies

Lunardini, Francesca; Luperto, Matteo; Daniele, Katia; Basilico, Nicola; Damanti, Sarah; Abbate, Carlo; Mari, Daniela; Cesari, Matteo; Ferrante, Simona; Borghese, N. Alberto

doi:10.1109/bhi.2019.8834513

Cited by 11 publications

(11 citation statements)

References 9 publications

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“…Such belief is also supported by the comparison between the answers (to (Q1), (Q3), (Q4), and (Q5)) obtained in the current study (Robot-assisted) with the results collected from a previous work of our group [28], in which the same digitized tests were performed at the Foundation IR-CCS Ca' Granda Ospedale Maggiore Policlinico of Milan by 21 cognitively-healthy older adults (age range: 69-84, Mean=76.3, Standard Deviation=4.3), under the supervision of a clinician (Clinician-assisted) (Table II). Since the two groups were tested on the same Test App, and given that they were characterized by comparable age range and technology acquaintance, the extremely similar results obtained to (Q4) are not surprising.…”

Section: B Results and Lesson Learnedsupporting

confidence: 80%

“…Besides this interaction, the test digitization allows for automated computation of the test evaluation metrics. Additional details on the implementations of our digital tests can be found in [28] where their validity (providing predicting capabilities comparable to those of their traditional counterparts) is shown.…”

Section: A Cognitive Assessment With Digitalized Testsmentioning

confidence: 99%

“…To do so, we devised an exploratory study involving potential future users. In addition, to distinguish the contribution of the two building blocks (digital tests and robotic supervision) on the obtained scores, the results were compared to the ones collected from a control group, in which potential users performed the digital tests in a clinical environment, under the supervision of an expert human operator [28]. In the following, we first detail our experimental setup and then we outline the results and lessons learned.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment

Luperto

Romeo²,

Lunardini

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact in our aging society, by means of digitalized cognitive tests. In this work, we present a system where a specific set of cognitive tests is selected, digitalized, and integrated with a robotic assistant, whose task is the guidance and supervision of the users during the completion of such tests. The system is then evaluated by means of an experimental study involving potential future users, in order to assess its acceptability and identify key directions for technical improvements.

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Section: B Results and Lesson Learnedsupporting

confidence: 80%

Section: A Cognitive Assessment With Digitalized Testsmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment

Luperto

Romeo²,

Lunardini

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…A future study should confirm our findings with a diagnosis based on biomarkers. Second, parts of the cognitive measures we used for the diagnosis and regression analysis were based on the tablet version of drawing tasks instead of the standard paper-and-pencil version; however, given the previous studies showing high correlations between the resulting scores of the tablet-and paper-based tests [70,71], its effect on our main findings is potentially small. Third, although to our knowledge this is the first study investigating combinations of gait, speech, and drawing data for detecting AD and MCI, the sample size was limited.…”

Section: Discussionmentioning

confidence: 97%

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer’s Disease and Mild Cognitive Impairment

Yamada

Shinkawa

Kobayashi

et al. 2021

JAD

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Background: Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD. Objective: We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI. Methods: Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants. Results: Combining all three behavioral modalities achieved 93.0%accuracy for classifying AD, MCI, and CN, and only 81.9%when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI. Conclusion: Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

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“…The user is expected to perform regularly two neuropsychological tests commonly used to detect early signs of cognitive impairment [9]. The role of the VC in this scenario is to detect when the tests need to be performed and present them for the user to perform.…”

Section: Neuropsychological Testsmentioning

confidence: 99%

A Virtual Caregiver for Assisted Daily Living of Pre-frail Users

Renoux

Luperto

Basilico

et al. 2020

Lecture Notes in Computer Science

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As Europe sees its population aging dramatically, Assisted Daily Living for the elderly becomes a more and more important and relevant research topic. The Movecare Project focuses on this topic by integrating a robotic platform, an IoT system, and an activity center to provide assistance, suggestions of activities and transparent monitoring to users at home. In this paper, we describe the Virtual Caregiver, a software component of the Movecare platform, that is responsible for analyzing the data from the various modules and generating suggestions tailored to the user's state and needs. A preliminary study has been carried on over 2 months with 15 users. This study suggests that the presence of the Virtual Caregiver encourages people to use the Movecare platform more consistently, which in turn could result in better monitoring and prevention of cognitive and physical decline.

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Validity of digital Trail Making Test and Bells Test in elderlies

Cited by 11 publications

References 9 publications

Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment

Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer’s Disease and Mild Cognitive Impairment

A Virtual Caregiver for Assisted Daily Living of Pre-frail Users

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