Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia. Hernandez, Netzahualcoyotl; Garcia-Constantino, Matias; Beltran, Jessica; Hecker, Pascal; Favela, Jesus; Cleland, Ian; Lopez, Hussein; Arnrich, Bert; McChesney, Ian in EAI Endorsed Transactions on Pervasive Health and Technology (2020). 5(19)
INTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving that affects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxiety and remain for extended periods, which affects their quality of life. OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during the manifestation of anxiety in patients with dementia. METHODS: We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structured interviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, we developed the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio to represent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety in patients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patients during episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network model trained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the two groups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved from publicly available datasets. RESULTS: The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitative results from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network and Support Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classification accuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%. CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre, qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of a methodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude by signalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.
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AbstractINTRODUCTION: Dementia is a syndrome characterised by a decline in memory, language, and problem-solving that affects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxiety and remain for extended periods, which affects their quality of life. OBJECTIVES: To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during the manifestation of anxiety in patients with dementia. METHODS: We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structured interviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, we developed the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio to represent two groups of sounds: (i) Disturbance which includes audio files that characterise sounds that trigger anxiety in patients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patients during episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network model trained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the two groups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved from publicly available datasets. RESULTS: The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitative results from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network and Support Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classification accuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%. CONCLUSION: In this paper, we presented the outcomes from an observational study in-site at a residential care centre, qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of a methodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude by signalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.
