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IDA The $5 Networked Digital Stethescope
The medical landscape is changing rapidly: technology is enabling individuals to perform many of the tasks once reserved only for medical professionals. The falling cost of sensors, computing power, and networking provide fertile ground for the advancement of cheap medical devices for home use. Complex and expensive medical devices are mainly used in medical facilities by health professionals. IDA is an attempt to disrupt this paradigm and introduce a new type of device: easy to use, low cost, and open source.
IDA is a 5$ digital stethscope that provides clinical quality auscultation, works over clothes as well as bare skin and is superior to existing commercial devices at a fraction of the cost. The 5$ digital stethoscope was manufactured using simple DIY digital fabrication technologies, to enable manufacturing in developing countries. The device enables people to record the sounds of their heart and lungs and transmit them to a remote medical professional or cloud service for diagnosis using a mobile phone, or computer.
The stethoscope is one of the most iconic tools used by physicians… a barrier patient-centered care. There is definitely nothing patient-centered about a clinician putting a stethoscope in his or her ears and proceeding to command a patient through a series of position changes and deep breaths only to conclude with “everything sounds fine” or “hmmm”. For patients to become truly active participants in their care, we not only need to rethink our communication and educational tools but also every medication instrument that we use. Sure, there are some patient-friendly versions of pedometers, sphygmomanometers, and glucometers coming to market, but what about the time-honored clinician tools like the otoscope, ophthalmoscope, and stethoscope. The patient should be able to hear everything that the clinician hears and see everything that the clinician sees. The instruments should be in every patient home as well. They should be inexpensive and easy to use. Even more important, they need to be reinvented so that they will fit into a modern model of healthcare delivery. They need to be networked with the ability to remotely control them and for them to stream data securely over the web. Patient and coach should be able to explore the data together and make comparisons not only over time but also across populations. -
FEEL
A System For Acquisition, Processing and Visualization of Biophysiological Signals and Contextual Information
State of the art technology has made it possible to monitor various physiological signals for prolonged periods. Using wearable sensors, individuals can be monitored, sensor data can be collected and stored in digital format, transmitted to remote locations, and analyzed at later times. Such a technology opens the door for a multitude of exciting and innovative applications. We could learn the effects of the environment and our day-to-day actions, and choices on our physiology. How do the number of sleep hours affect our activity levels during the following day. How do the times we schedule our meals impact our performance? How does physical activity affect our quality of sleep? Do such choices have an impact on chronic conditions? Physiological signals are only part of the information required to answer such questions. It is necessary to label these signals with contextual information. Context is defined as: “the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed”. Ideally, one would want to have access to full contextual information alongside with the sensor data. This may include location, activity, nutrition, social interactions, etc.
If we are to learn the effects of the environment and our day-to-day actions, and choices on our physiology, we must develop systems that will label biophysiological senor data with contextual information. FEEL is a system for the acquisition, processing and visualization of biophysiological signals and contextual information. The system comprises a mobile client application (FMC) and a backend server; The mobile client collects contextual information: phone call details, email reading details, calendar entries, and user location at a fixed interval that is transmitted to the backend server. The backend server stores the contextual information and biophysiological signal data that is uploaded by the user, processes the information and provides a novel interface for viewing the combined data.
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Frequent EDA and Event Logging
a Mobile Social Interaction Stress Monitoring System
Have you ever wondered which emails, phone calls, or meetings cause you the most stress or anxiousness? Well, now you can find out. A commercial wristband sensor measures Electro-Dermal Activity (EDA), which responds to stress, anxiety, and arousal. Each time you read an email, place a call, or hold a meeting, your phone will measure your EDA levels by connecting to the sensor via Bluetooth.
The Q Curve EDA sensor (by Affectiva)
The system provides a tool that enables the user to attribute levels of stress and anxiety to particular events. It allows the user to view all of the events and the levels of EDA that are associated with them: users can see which event caused a higher level of anxiety and stress, and can view which part of an event caused the greatest reaction. Users can also view EDA levels in real time. -
PMQ Pain Management Quantified
Historically pain management mainly relies on patient subjective reports. This method makes it very difficult to efficiently titrate medication doses. In addition, this imposes significant burden on the patient that needs to manage manual pain dairies. The goal of PMQ is to provide a method for objectively measuring pain in a quantitative manner. The system is comprised of an Android application, a wearable sensor to measure skin conductance and an instrumented pill box that records medication intake and transmits the records to the care giver. The android application enables the patient to mark ‘pain points’ in real-time on a manikin as well as provide historical data on pain levels and medication intake. This approach provides comprehensive and important information for finding efficient pain management solutions. Now instead of relying on how patients recount their experiences with pain, doctors can use the data that is collected by the sensor in combination with the patient self-report data to determine the best course of treatment.
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