healthcare technology

People often turn to technology to manage their health and wellbeing, whether it is

• to record their daily exercise,
• measure their heart rate, or increasingly,
• to understand their sleep patterns.

Sleep is foundational to a person’s everyday wellbeing and can be impacted by (and in turn, have an impact on) other aspects of one’s life — mood, energy, diet, productivity, and more.

As part of Google's ongoing efforts to support people’s health and happiness, Google has announced Sleep Sensing in the new Nest Hub, which uses radar-based sleep tracking in addition to an algorithm for cough and snore detection.

The new Nest Hub, with its underlying Sleep Sensing features, is the first step in empowering users to understand their nighttime wellness using privacy-preserving radar and audio signals.

Understanding Sleep Quality with Audio Sensing

The Soli-based sleep tracking algorithm gives users a convenient and reliable way to see how much sleep they are getting and when sleep disruptions occur.

However, to understand and improve their sleep, users also need to understand why their sleep is disrupted.

To assist with this, Nest Hub uses its array of sensors to track common sleep disturbances, such as light level changes or uncomfortable room temperature. In addition to these, respiratory events like coughing and snoring are also frequent sources of disturbance, but people are often unaware of these events.

As with other audio-processing applications like speech or music recognition, coughing and snoring exhibit distinctive temporal patterns in the audio frequency spectrum, and with sufficient data an ML model can be trained to reliably recognize these patterns while simultaneously ignoring a wide variety of background noises, from a humming fan to passing cars.

The model uses entirely on-device audio processing with privacy-preserving analysis, with no raw audio data sent to Google’s servers. A user can then opt to save the outputs of the processing (sound occurrences, such as the number of coughs and snore minutes) in Google Fit, in order to view personal insights and summaries of their night time wellness over time.

read the entire unedited blog post at https://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html

A new, noninvasive method for measuring brain blood flow with light has been developed by biomedical engineers and neurologists at the University of California, Davis, and used to detect brain activation.

The new method, functional interferometric diffusing wave spectroscopy, or fiDWS, promises to be cheaper than existing technology and could be used for assessing brain injuries, or in neuroscience research.

The human brain makes up 2% of our body weight but takes 15% to 20% of blood flow from the heart. Measuring cerebral blood flow is important for diagnosing strokes, and for predicting secondary damage in subarachnoid hemorrhages or traumatic brain injuries. Doctors who provide neurological intensive care, would also like to monitor a patient's recovery by imaging brain blood flow and oxygenation.

Existing technology is expensive and cannot be applied continuously or at the bedside. For example, current techniques to image cerebral blood flow require expensive MRI or computed tomography scanners. There are light-based technologies, such as near-infrared spectroscopy, but these also have drawbacks in accuracy.

The new method takes advantage of the fact that near-infrared light can penetrate through body tissues. If you shine a near-infrared laser on someone's forehead, the light will be scattered many times by tissue, including blood cells. By picking up the fluctuation signal of the light that finds its way back out of the skull and scalp, you can get information about blood flow inside the brain.

read more at https://medicalxpress.com/news/2021-05-brain-blood.html