From polysomnography and actinography, to bed sensors, wireless EEG, smartwatches, fitness trackers, mobile phones sensing, ultrasound.

From polysomnography and actinography, to bed sensors, wireless EEG, smartwatches, fitness trackers, mobile phones sensing, ultrasound sensors, WiFi and radio-signal approaches

Cost of the short sleepers and solutions

Sleep is a crucial biological process — that has long been recognised as an essential determinant of human health and performance — known to be regulated by three main factors: circadian rhythms, sleep–wake homoeostasis and cognitive-behavioural influences.

With regards to behavioural determinants, poor sleep quality has been associated with stress, anxiety, smoking, sugary drink consumption, financial concerns, workplace pressures, regularity of working hours, physical activity, and commuting times. As a result sleep deprivation is kind of a (silent) epidemic nowadays.

In a 2016 report RAND Corporation —an American nonprofit global policy think tank — quantified that the economic costs of insufficient sleep across five OECD (Organisation for Economic Co-operation and Development) countries (Canada, USA, UK, Germany and Japan) exceeds $600 billion a year, and that can result in large economic costs in terms of lost GDP and lower labour productivity.

These costs will only rise over time — assuming a constant proportion of short sleepers in the future — and sleep deprivation will continue adversely affecting individuals, through negative effects on their health and wellbeing, apart being costly for employers which is associated with large economic losses. Therefore, solving the problem of insufficient sleep represents a potential “win-win” situation for individuals, employers and the wider society.

For this reason novel digital technologies for the study, monitoring and modulation of sleep (data acquisition, data storage and curation, data processing and modelling) are emerging continuously, and big data on sleep science is now “The SleepOMICS”.

SleepOMICS begins with the acquisition of sleep-related data using a variety of sensors. This data is then stored and curated (filtering and anonymisation, smoothing and de-noising can remove unwanted spikes, trends and outliers from a signal, re-sampling and standardising can be used to improve data integrity and consistency in the pre-processing stages). Once sleep data has been processed, data modelling can be commenced. Many of these modelling and application tasks are based on AI, while machine learning provides a more flexible alternative to data modelling, especially when applied to the raw unstructured signals.

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In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

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