Activity and emotion recognition to support diagnosis of psychiatric diseases

Tampere, January 30th B. Arnrich

Activity and Emotion Recognition to Support Early Diagnosis of Psychiatric Diseases

Activity and Emotion Recognition to Support Early

Diagnosis of Psychiatric Diseases

Pervasive Health ConferencePervasive Health Conference

Tampere, 30Tampere, 30thth January 2008 January 2008



David Tacconi, Oscar Mayora, CREATE-NET

Paul Lukowicz, University of Passau

Bert Arnrich, Cornelia Setz, Gerhard Tröster, ETH Zurich

Christian Haring, PSYCHIATRIC STATE HOSPITAL TIROL (PSHT)

Paper ContributorsPaper Contributors

PSHTPSYCHIATRIC STATE HOSPITAL TIROL



OutlineOutline

IntroductionBipolar disorder Objectives and System RequirementsPervasive computing to support Bipolar

Disorder diagnosisA proposed System ArchitectureDiscussion and future work



IntroductionIntroduction Global Burden of Disease

Mental illness accounts for over 15% of the burden of diseases in established market economies (source World Health Organization, World Bank, Harvard University)

Disability Adjusted Life Years (DALYs): Measure the lost years of healthy life (premature death or

disability) Depression:

is the most common psychiatric disorder, accounting for 50.8 million DALYs or 10.7% of the global burden of disease

It is ranked fourth among all causes of DALYs and is the leading nonfatal condition globally

Bipolar disorder: account for another 14,1 million (3.0%) DALYs



IntroductionIntroduction Few technological solutions exist to aid people

affected by mental illness. Obvious reasons are:

people affected by mental illness are more likely to have problems dealing with complex technology

providing behavioral assistance is much more difficult than providing physical assistance

solutions require considerable amount of domain specific knowledge and can only be developed in close cooperation with psychiatrists



Bipolar DisorderBipolar Disorder Bipolar disorder

repeated relapses of mania and depression Recurrence rates are high at around 50% at one year and 70% at

four years Treatments for Bipolar disorder:

Pharmacotherapy is the main treatment nowadays Alternative: teach the patients to recognize and manage Early

Warning Signs (EWS) Diagnosing through patient questionnaires:

for depression the Hamilton Depression Scale (HAMD) for mania the Bech-Rafaelsen Mania Scale (BRMS) Both contain a series of questions related to patients’ activities and

feelings



Bipolar Disorder: the HAMDBipolar Disorder: the HAMD1. Depressed Mood2. Feelings of Guilty3. Suicide4. Insomnia (early)5. Insomnia (middle)6. Insomnia (late)7. Work and Activities8. Retardation:

Psychomotor9. Agitation10. Anxiety (Psychological)

11. Anxiety Somatic12. Somatic Symptoms

(Gastrointestinal)13. Somatic Symptoms

General14. General Symptoms15. Hypocondriasis16. Loss of Weight17. Insight18. Diurnal Variation19. Depersonalization and

Derealization20. Paranoid Symptoms

Evaluate between 0 and 4 to obtain the total score



Bipolar Disorder: the BRMSBipolar Disorder: the BRMS1. motor activity2. verbal activity3. flight of thoughts4. voice/noise level5. hostility/

destructiveness6. mood and

feelings of well-being

7. self-esteem

8. contact

9. sleep (based on the average of the previous 3 nights)

10.sexual interest and activity

11.work levelEvaluate between 0 and 4 to obtain the total score



ObjectivesObjectives We identify Bipolar Disorder as a condition that can

realistically benefit from behavioral monitoring We identify support in early detection of imminent transitions

between normal, manic and depressed states as the specific contribution to therapy

We identify specific behaviors that need to be detected by the proposed system, using the so called Hamilton Depression Scale (HAMD) and Bech-Rafaelsen Mania scale (BRMS), which are generally accepted tools in the diagnosis of depression and mania

Based on literature study and previous work by the authors, we argue that detecting these specific behaviors is feasible

We propose an appropriate system architecture based on existing devices and previous systems implemented by the authors groups



Pervasive Computing for diagnosis of BDPervasive Computing for diagnosis of BD From our experience and a literature study, we propose to

look at the following parameters: HAMD:

items 4,5,6 (insomnia) 7 (work and activities) 8 (psychomotoric retardation) 9 (agitation) 10, 11(anxiety)

BRMS: items 1 (motoric activities) 2 (verbal activities) 4 (voice-noise level) 8 (contacts) 9 (sleep)



Early Diagnosis 1/3Early Diagnosis 1/3 Insomnia and Sleep disorders (HAMD 4-6, BRMS 9)

”Gold standard” (laboratory settings): polysomnographic monitoring of sleep time physiological parameters (e.g. respiration, heart rate variability) and sleep

motion On-body sensors:

unobtrusively embedded into biomedical clothes or mattresses allow to obtain preliminary diagnosis and to perform more frequent tests

under real-life conditions Other solutions:

thin film, dynamic quasi-piezoelectric sensors placed under the mattress capacitive pressure sensor mat would allow monitoring sleep motion



Early Diagnosis 2/3Early Diagnosis 2/3 Verbal activities (BRMS 2, 4),

Contacts/Conversation (BRMS 8)

automatic speech character identificationwould allow to extract features describing contextual side

informationspoken messages convey besides information on

characteristics as intonation, speaking rate or emotional state.

emotion recognition can give to the therapists information about variation of the patient’s mental state.



Early Diagnosis 3/3Early Diagnosis 3/3 Activity Recognition (HAMD 7,8,9,10,11, BRMS 1)

Several past works on activity recognition: it remains unclear how most prior systems will perform under real

life conditions

Based on previous experience, we target systematic real life trials to: quantify his Work and Activities (HAMD 7 and BRMS 1) understand the Agitation (HAMD 9) and Anxiety (HAMD 10, 11) he

experiences measure an eventual Psychomotoric Retardation (HAMD 8)



System Architecture System Architecture Constraints to be considered:

patients are likely to reject pervasive computing technology in principle

target devices should be as less obtrusive as possible patients cannot be asked to perform any training of devices, and

this complicates things for emotion and activity recognition. Activity and emotion recognition is targeted to medium and

long term behavior Higher errors in activity recognition are allowed doctors are more interested in average behaviors rather than in

instantaneous activity pattern or emotions a patient is feeling in a given moment.

Doctors are interested in behaviors that are repeated in time and that can be symptoms of disease’s relapse.



System ArchitectureSystem ArchitectureThe User Interfaces module:•present persuasive feedback to the users for motivating healthier patients’ behavior

The User Model includes all patient’s characteristics, disease’s peculiarities and his preferences. Information stored in:•User Profile (UP)•Disease Description (DD)•Patient Description (PD)

The Context Acquisition module gathers data from Sensors and is driven by:•Emotion Recognition Manager that selects sensors for emotion recognition•Activity Recognition Manager that selects sensors for recognizing user’s activity•User model manager gives proper inputs

The Content Manager module is responsible:•For uploading the data to the EMR through the Data Upload module•For presenting information to the patient through the Feedback Manager module



Activity recognitionActivity recognition



Emotion RecognitionEmotion Recognition



Discussion and future workDiscussion and future work We have shown a feasibility study on applying existing pervasive computing

techniques to support the early diagnosis of bipolar disorder We have formulated proper system requirements, showing then how current

research and authors’ expertise can be leveraged for helping doctors and patients in recognizing early symptoms of depression and mania

We have also defined a system architecture meeting such requirements Future work:

Refine the P-cube platform implementation for the bipolar disorder scenario Integrate the implementation with indoor environment (e.g. Living Lab at Create-

Net) including hand-over between outdoors and indoors Integrate the context recognition platforms we have in the context data block Continue in a close cooperation with doctors to define proper UI and medical

records Test-bed trials to refine the system (in cooperation with doctors) Start working with patients for developing a first proof of concept of the proposed

system



Back-up slides: Mania SymptomsBack-up slides: Mania Symptoms Increased energy, activity, and restlessness Excessively "high," overly good, euphoric mood Extreme irritability Racing thoughts and talking very fast, jumping from one idea to another Distractibility, can't concentrate well Little sleep needed Unrealistic beliefs in one's abilities and powers Poor judgment Spending sprees A lasting period of behavior that is different from usual Increased sexual drive Abuse of drugs, particularly cocaine, alcohol, and sleeping medications Provocative, intrusive, or aggressive behavior Denial that anything is wrong

List taken from: http://www.nimh.nih.gov/publicat/bipolar.cfm



Back-up slides: Depression SymptomsBack-up slides: Depression Symptoms Lasting sad, anxious, or empty mood Feelings of hopelessness or pessimism Feelings of guilt, worthlessness, or helplessness Loss of interest or pleasure in activities once enjoyed, including sex Decreased energy, a feeling of fatigue or of being "slowed down" Difficulty concentrating, remembering, making decisions Restlessness or irritability Sleeping too much, or can't sleep Change in appetite and/or unintended weight loss or gain Chronic pain or other persistent bodily symptoms that are not caused by

physical illness or injury Thoughts of death or suicide, or suicide attempts

iopList taken from: http://www.nimh.nih.gov/publicat/bipolar.cfm



Existing devices: Existing devices: QBIC Belt Integrated ComputerQBIC Belt Integrated Computer

Key Features XScale CPU up to 400Mhz 256 MB SDRAM Low power Standard interfaces (USB,

Serial, VGA, Bluetooth)

Applications Data recording with on-line

processing and communication



Existing devices: SensorbuttonExisting devices: Sensorbutton

SensorsAccelerometers, light

sensor, microphone, microprocessor

ApplicationsOn-line context

recognition

Algorithmsnearest-neighbor and

C4.5 decision tree



Existing devices: Textile Pressure SensorExisting devices: Textile Pressure Sensor

Pure textile, capacitive sensor

Individually connected electrodes using silver coated yarn

Localized detection of pressure distribution

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Activity and emotion recognition to support diagnosis of psychiatric diseases

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