phat 19(19): e5

Research Article

Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia

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  • @ARTICLE{10.4108/eai.10-2-2020.163097,
        author={Netzahualcoyotl Hernandez and Matias Garcia-Constantino and Jessica Beltran and Pascal Hecker and Jesus Favela and Joseph Rafferty and Ian Cleland and Hussein Lopez and Bert Arnrich and Ian McChesney},
        title={Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={smart microphones, anxiety, dementia, sound recognition},
  • Netzahualcoyotl Hernandez
    Matias Garcia-Constantino
    Jessica Beltran
    Pascal Hecker
    Jesus Favela
    Joseph Rafferty
    Ian Cleland
    Hussein Lopez
    Bert Arnrich
    Ian McChesney
    Year: 2019
    Prototypical System to Detect Anxiety Manifestations by Acoustic Patterns in Patients with Dementia
    DOI: 10.4108/eai.10-2-2020.163097
Netzahualcoyotl Hernandez1,*, Matias Garcia-Constantino1, Jessica Beltran2, Pascal Hecker3,4, Jesus Favela5, Joseph Rafferty1, Ian Cleland1, Hussein Lopez5, Bert Arnrich3, Ian McChesney1
  • 1: Ulster University, Belfast, Northern Ireland, United Kingdom
  • 2: CONACYT-IPN (Instituto Politecnico Nacional), Tijuana, Mexico
  • 3: Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
  • 4: audEERING GmbH, Gilching, Germany
  • 5: CONACYT-CICESE (Centro de Investigacion Cientifica y de Educacion Superior de Ensenada), Ensenada, Mexico
*Contact email:


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.