continuous monitoring of mental disorders and progressive neurological diseases

inCognita helps detect signals related to mental disorders and progressive neurological diseases. inCognita brings real-time monitoring that can facilitate earlier diagnosis and more effective treatments.

inCognita is a unique data science framework that Volv have developed.

Detecting neurocognitive and neurodegenerative disorders using smartphones and machine learning

Why? Because cognitive and neurodegenerative diseases require constant care . . .

Bak constantcare 3
Image of male looking depressed

Continuous monitoring, with consent, is now possible unobtrusively

The ubiquity of smartphones in modern life suggests the possibility to use them to continuously monitor patients, for instance to detect undiagnosed diseases or track treatment progress. Such data collection and analysis may be especially beneficial to patients with

  1. mental disorders, as these individuals can experience intermittent symptoms and impaired decision-making, which may impede diagnosis and care-seeking, and
  2. progressive neurological diseases,

as real-time monitoring could facilitate earlier diagnosis and more effective treatment.

Volv has developed a new method of leveraging passively-collected smartphone data and machine learning to detect and monitor brain disorders such as depression and Parkinson’s disease. Crucially, the Volv inCognita framework is able learn accurate, interpretable models from small numbers of labeled examples: i.e., smartphone users for whom sensor data has been gathered and disease status has been determined.

Predictive modeling is achieved by learning from both real patient data and ‘synthetic’ patients constructed via adversarial learning. The inCognita framework is shown to outperform state-of-the-art techniques in experiments involving disparate brain disorders and multiple patient datasets.

Crucial to management of mental health is the ability to both detect appropriate intervention points as well as determine context sensitive interventions. Importantly, Conginitive Behavioural Therapy (CBT) has been shown to have significant impact on reducing disease progression and is a digital intervention that can be triggered in a context sensitive way through inCognita.

More about inCognita

the inCognita approach


inCognita example business case

inCognita helps detect signals related to mental disorders and progressive neurological diseases. inCognita brings real-time monitoring that can facilitate earlier diagnosis and more effective treatments. Here we present a business case relevant to Switzerland

Initial therapeutic areas where inCognita has been applied:

Major depressive disorder

Depression is a leading cause of disability worldwide, estimated to affect more than 350M people [16]. The DSM [17] lists nine symptoms commonly associated with major depressive disorder: depressed mood, diminished interest and pleasure in activities, fatigue, restlessness, sleep change, weight change, diminished ability to concentrate, feelings of worthlessness, and thoughts of death and suicide. Several of these symptoms appear to have proxies which can be continuously and conveniently inferred from the outputs of sensors available in (standard) smartphones. This observation indicates it may be possible to recognize depression through computational analysis of passively-gathered smartphone data, and indeed this possibility has attracted the attention of clinicians and researchers (see, for example, [1,2,4-9] and references there-in).

inCognita MDD Results

Parkinson's disease

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide, and incidence is rising [18]. While current treatment strategies aim to improve symptoms, increasing effort is being devoted to developing therapies which can slow or prevent PD progression. Such proactive treatment methods are likely to be most effective when used early in the disease process, before substantial neuronal loss has occurred [19]. However, existing methods for detecting the onset of PD are invasive and expensive, impeding early diagnosis [e.g. 13,18,19]. Symptoms of PD such as motor disfunction and speech impairment may have proxies which can be conveniently and in-expensively measured with smartphone sensors, suggesting the potential to efficiently recognize PD through analysis of smartphone data [10-15].

inCognita Parkinson's disease results

InCognita References

References listing for the inCognita product