Category: mHealth

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Apps Data fitness mHealth Monitoring Sensors Sleep Wearables

Home medical device data uploaded to EHRs; patient participation encourages behavior modification

http://www.bostonglobe.com/lifestyle/health-wellness/2013/07/28/century-house-call/tdupWvOQI6b3dKdKcEgdGM/story.html

Boston’s Partners HealthCare has launched a system that allows patients to upload information from their medical devices directly to their electronic records in doctors’ offices.  Patients can regularly use glucometers, blood pressure cuffs, bathroom scales, and pulse oximeters at home, and send the data to their doctors.  Doctors are also becoming increasingly interested in eating habit, movement, and sleep data collected by patients using consumer health-tracking devices.

A recent study by the company showed a significant decrease in diastolic blood pressure among participants who both took their readings and uploaded them to a web interface where they could track and monitor their progress.

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Data Eyes mHealth Monitoring Wearables

Contact lens continuously monitors intra-ocular pressure

http://www.sensimed.ch/en/products/sensimed-triggerfishr.html

The Sensimed Triggerfish combines a non-invasive wireless soft contact lens sensor with an automated system for recording IOP related patterns for up to 24 hours. The ambulatory patient wears the device during normal activity, including sleeping.  At the end of the session, the data is transferred from the recorder to an ophthalmologist’s computer for analysis of the circadian IOP-related pattern.

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Machine Learning mHealth Monitoring Sensors

Tooth sensor monitors health

http://mll.csie.ntu.edu.tw/papers/TeethProbeISWC.pdf

National Taiwan University researchers have created a tooth-based sensor, accelerometer, and associated machine learning software to detect and distinguish between chewing, smoking, coughing, or speaking.  The capability to monitor mouth motions may help physicians keep track of patient progress or allow a patient to better understand his/her health habits.  Working prototypes of the tooth sensor used wires to connect to a power source and a data-logging device.  Inventor Hao-hua Chu and colleagues envision the tooth sensor eventually being powered by a micro-battery and transmitting data wirelessly via Bluetooth to a smartphone. They also hope to embed the device inside artificial teeth that can be easily removed and customized for each individual.

It likely that the same technology could be used to monitor infection level in wound healing in the form of an adhesive sensor.

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Apps Crowdfunding mHealth Monitoring Sensors

uChek crowdfunds smartphone urine analysis system

http://uchek.in

Biosense’s uChek is crowdfunding its urine analysis system.  It is a free app for iPhone users that can interpret color changes on urine analysis test strips with a smartphone camera. uChek can be used for semi-quantitative and qualitative detection of leukocytes, ketone, nitrite, urobilinogen, bilirubin, protein, specific gravity and pH. Test results may provide information regarding the status of carbohydrate metabolism, kidney and liver function, acidbase balance and bacteriuria.

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Apps Heart mHealth Sensors

Stethoscope add-on improves patient assessment and diagnosis

http://rijuven.com/cardiosleeve.html

CardioSleeve, when attached to a stethoscope, records, displays and analyzes electrical and acoustical footprints of the heart in real time via any wirelessly connected portable device.    The data can be stored in the cloud or uploaded into an EHR.

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Apps Eyes mHealth

Smartphone diagnostic and cloud platform make eye care accessible

http://eyenetra.com/netra-g.html

Vinod Khosla and others have invested in MIT Media Lab’s EyeNetra, a smartphone attachment that claims to diagnose nearsightedness, farsightedness and astigmatism.  The device is positioned as a less bulky alternative to the Shack-Hartmann Wavefront sensor.  A $2 eyepiece is clipped onto a phone.  The user then clicks to align the displayed patterns.  The number of clicks required to bring the patterns into alignment indicates the refractive error.  Patients connect to corrective lens providers through a cloud based system.  The technology provides access to eye exams in the developing world.

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Brain mHealth Monitoring Sensors

Sports sensors warn of head injury

http://www.nytimes.com/2013/06/16/business/a-wearable-alert-to-head-injuries-in-sports.html?_r=0

The New York Times reports on the growing trend of sensor based protection/early warning systems for athletes. The devices, packed with sensors and microprocessors, register a blow to a player’s skull and immediately signal the news by blinking brightly, or by sending a wireless alert.  Algorithms evaluate the impact and determine severity.

 

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mHealth Monitoring

Emotionally reactive avatars treat patients at home

http://www.technologyreview.com/news/514881/the-avatar-will-see-you-now/

Remote monitoring systems are increasing able to capture a patient’s state of mind and body.

Using Sense.ly’s platform, patients can communicate their condition to an emotionally reactive avatar through their phone, desktop, or TV. The avatar asks the patient simple questions, and if programmed by a doctor, it can also answer questions—such as what a diabetes patient with high blood-sugar readings should eat that day. The software also collects data from other medical devices that a patient uses, such as a glucose meter, and can capture gestures with Kinect. The reports sent to the doctor include red-flag notifications to act on right away; charts, graphs, and analytics tracing the patient’s progress over time; and a transcript of the voice interaction.

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Apps fitness mHealth Monitoring

mHealth timeline, 2009 – 2013

http://mobihealthnews.com/22674/timeline-smartphone-enabled-health-devices/

Sensors are becoming smaller, smarter, and more ubiquitous,  and have transformed the way we monitor our health.  Attached is a timeline of health and fitness apps from 2009 through today, providing an interesting look at the development of the mHealth market.

Categories
Apps Asthma fitness mHealth Monitoring Sensors Sleep

Mobile phone microphones as health sensors

http://www.economist.com/news/technology-quarterly/21578518-sensor-technology-microphones-are-designed-capture-sound-they-turn-out

The Economist’s Technology Quarterly describes how mobile phone microphones are being used as versatile sensors with myriad health applications.  Examples follow:

1. Professor Tanzeem Choudhury of Cornell has created StressSense to capture and analyze voice characteristics such as amplitude and frequency. Her team concluded that “it is feasible to implement a computationally demanding stress-classification system on off-the-shelf smartphones”.

2. BeWell’s sleep-tracking feature, also by Professor Choudhury,  determines whether the phone’s user is awake or not by analyzing usage, light and sound levels, and charging habits. Physical activity is monitored using built-in accelerometers for motion-detection. Social activity is measured by sounds that indicate that the user is talking to someone, either in person or over the phone.

3. Professor John Stankovic of the University of Virginia uses microphones to capture heartbeats. Researchers in his group use earphones modified with accelerometers and additional microphones that detect the pulse in arteries in the wearer’s ear. This makes it possible to collect physical state information, including heart rate and activity level, which is transmitted to the smartphone via the audio jack.

4. Shwetak Patel of the University of Washington uses a smartphone to measure lung function when users blow on its microphone. His team has developed the SpiroSmart app, which simulates a digital spirometer, to measure the volume of air a person can expel from his or her lungs. Spirometers help doctors better understand the health status of patients with conditions like asthma, chronic obstructive pulmonary disease and cystic fibrosis.

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Apps mHealth Monitoring Sensors

Smartphone as handheld biosensor

http://news.illinois.edu/news/13/0523iphone_biosensor_BrianCunningham.html

University of Illinois researchers have developed an iPhone cradle and app that uses its camera and processing power as a biosensor to detect toxins, proteins, bacteria, viruses and other molecules.    Professor Brian Cunningham, the team’s leader, discussed healthcare applications:  “A lot of medical conditions might be monitored very inexpensively and non-invasively using mobile platforms like phones. They can detect molecular things, like pathogens, disease biomarkers or DNA, things that are currently only done in big diagnostic labs with lots of expense and large volumes of blood.”

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Brain Computer Vision mHealth Monitoring Sensors

Computer vision algorithms used to diagnose depression

http://medvr.ict.usc.edu/projects/dcaps/

SimSensei software, developed by Stefan Scherer and colleagues at the University of Southern California, combines computer vision algorithms and the psychological model of depression. An on-screen psychologist asks you a series of questions and watches how you physically respond. Using Kinect, the computer vision algorithms build up a very detailed model of your face and body, including your “smile level,” horizontal gaze and vertical gaze, how wide open your eyes are, and whether you are leaning toward or away from the camera. From these markers, SimSensei can determine whether you’re exhibiting signs that indicate depression — gaze aversion, smiling less, and fidgeting.