If you will be at CHI or Austin and would like to chat, send me an email. I enjoy going to conferences to attend presentations, but the real value to me is in the exchange of ideas, discussions and introductions facilitated by being in the same place at the same time.

The key message here is that health-focused sensors will be big. They project hundreds of millions of devices by 2014.

At the high-end, there are netbooks. Any BestBuy can sell you a complete netbook system for less than $300. For certain applications in data sensing, processing and communication, $300 is good enough. Unfortunately, a netbook is a bit too big and power hungry. Not to mention that its screen, graphics card and other features might go unused, inflating the cost of the device unnecessarily considering the job it’s been designated to do.

At the other end of the spectrum are platforms like the Arduino, which is small, inexpensive, but might not provide the processing power one might need. Are there any other alternatives? There are plenty of single-board computers out there, one of which is the Chumby Hacker Board, or CHB for short.

Recently, I’ve been following the development of an ARM-based platform called Raspberry Pi. The goal is to develop the cheapest possible computer with a basic level of functionality, for around $25. The team is already showing a prototype board, the size of a credit card, running Ubuntu:

Lots of details can be found here and they are expected to be shipping in December. I am really looking forward to experimenting with them.

This story also refers to a few online services that also provide video consultations.

These new channels are palliative measures towards fixing a broken system. It’s unclear what kind of impact they will have on families struggling to pay for healthcare. Probably none, since they are too expensive. But at least it shows that alternative approaches are being tried and technology explorations are under way. it’s a step in the right direction.

I found it particularly interesting for two main reasons. First of all, it examines the effectiveness of telemonitoring in a very rigorous study – a multicenter, randomized, controlled study involving patients hospitalized with heart failure. And secondly, it concludes that telemonitoring did not improve outcomes, despite what smaller studies involving telemonitoring suggested in the past.

Patients were enrolled from 2006 to 2009 at 33 cardiology practices across the U.S. The telemonitoring system employed was Tel-Assurance by Pharos Innovations.

The telemonitoring group was instructed to make daily, toll-free calls to the system. During each call, patients heard a series of questions about general health and heart-failure symptoms, and they entered responses using the telephone keypad.

To make sure that patients would use the telemonitoring system, patients were told that their health information would be reviewed by clinicians. Interestingly, an undesirable consequence of telemonitoring was a delay in seeking care for urgent situations because of a belief that clinicians would be immediately alerted if problems occurred.

A total of 85.6% of patients in the telemonitoring group made at least one call; among these patients, adherence to the intervention was highest, 90.2%, during the first week of the study period and decreased to 55.1% by week 26

I believe the low telemonitoring adherence in the long-term must be attributed to the lack of engagement associated with the Tel-Assurance system. Calling a phone number every night and answering health questions is not exactly engaging or fun.

As described in the article, the telemonitoring methodology selected was chosen because it is a standard today. However, one should not draw conclusions about the future of telemonitoring based on today’s technologies. After all, there’s an emerging generation of consumer health logging devices (e.g. Withings Wifi Scale, Zeo) that makes self-monitoring and telemonitoring practical and semi-automatic.

This technology was conceived and originally designed by Shwetak Patel, Joe Froehlich, Eric Larson and others at the University of Washington.

A few weeks ago, while at the CDC Public Health Informatics Conference, a question was posed to me: “What is the Hydrosense coverage right now considering that not all homes in the US are single-family homes and not all of them are connected to a water supply system?”. I was intrigued by this question and decided to investigate further. Luckily I didn’t have to go very far. Wikipedia and the U.S. Census Bureau had all the numbers I was looking for.

There are 115.9 million homes in the US. About 70 million of these (60.3%) are detached single-family units. Eighty percent of single-family homes are occupied by owners. In terms of water supply, 14.5% of Americans rely on their own water sources, typically wells. Water well pumps are used in this case. The pump sends water to a storage tank with an air bladder that compresses as the water is pumped in. At 40-60psi, the pump stops. When water is used in the home, pressure drops and when it goes below 20psi, the pump starts again.

More than 99% of the US has access to “complete plumbing facilities”, defined as having (1) hot and cold piped water, (2) bathtub or shower, and (3) flush toilet. Homes that lack such water facilities total 670,986, and are usually inhabited by the elderly, the poor and those living in rural areas. Alaska has the highest percentage of households without plumbing.

To sum it up, Hydrosense can be used today in 60% of homes in the US. We would like to enhance it so that it can work reliably in multi-family homes and apartment complexes as well, which will expand Hydrosense’s coverage to virtually every home in the country. Thinking globally, and especially in the context of developing countries, I now wonder how suitable Hydrosense is in other regions of the world.

The field of orthopedics is starting to reap the benefits of ubiquitous computing. While over at MedGadget, I found out about OrthoSensor, a company that is developing a platform around embedded sensing for joint replacement implants. Among other benefits, this system will make it possible to wirelessly monitor the bio-mechanical performance of a knee replacement over the long term, for example.

Together with capsule endoscopy and digestible sensors, embedded bio-mechanical analysis represents another step towards the future and mainstream reality of bioelectronics.

This post is my attempt to capture some of the activity in this space. This is not an exhaustive list, but I hope it’s representative of where we are. It combines devices and sensors that are fairly mainstream with hundreds of thousands of users, with others that are still in their infancy as research projects. I might update this list sporadically, based on new findings. I am also curious to find out what is missing from the list, so feel free to leave comments.

Activity Tracking

BodyMedia: The BodyMedia FIT system provides activity, calories and sleep pattern data. The BodyMedia FIT Armband automatically tracks the calories burned during daily activities and monitors the quality of your sleep. This is a fairly popular device and has been around for quite some time. As the name suggests, it is attached to the body through an armband and collects movement data.

Basis: Basis is a wristwatch that tracks caloric burn, activity levels and sleep habits. It’s not available yet, but it looks like it will be soon. The form factor is promising, and looks good too. It is supposed to contain an optical blood flow sensor, obtain temperature readings, and more.

Fitbit: The Fitbit tracks calories burned, steps taken, distance traveled and sleep quality. It’s a small device that can be clipped to a belt. It’s also quite popular, and available for purchase today.

Jawbone Up: New upcoming product announced by Jawbone, the company behind highly-rated headsets. It will track movement, sleep patterns and eating habits and will have the form factor of a bracelet.

Valencell Healthset: Earbuds that track heart rate, calories burned and physical activity. If this really works as advertised, it has enormous potential, since it doesn’t require any additional sensing devices like the BodyMedia FIT and the Fitbit.

Zephyr: The key product from Zephyr is called BioHarness, which measures critical vital signs (ECG, heart rate, breathing rate, skin temperature ) and physical activity using an accelerometer. The activity and physiological data can be transmitted in real-time for remote condition monitoring. They don’t seem to be selling a consumer product right now.

24eight: This company is developing a whole range of technologies associated with health sensing, from “smart” slippers (with “smart” insoles – slippers that can tell when your grandmother might be headed for a fall) to SIDSense, a 3D infant mobility monitor sensor. From what I can tell, 24eight is developing technologies and licensing it to be used in various products.

Sleep

Zeo: Zeo is the leader in the space of sleep analytics. After purchasing a $200 kit, you wear a headband when you go to bed. In the morning, the Zeo scores the quality of your sleep and shows you detailed analytics of how you slept through the night. A feature I find compelling is the alarm clock, which wakes you up within a time window, whenever it senses you will be most refreshed.

WakeMate: Similar to the Zeo, but instead of a headband, you must wear a bracelet. They claim their analytics platform “optimizes your waking hours by automatically analyzing your sleep and illuminating personal habits that affect your sleep”.

Lark: Similar to the Zeo and the WakeMate. Lark also markets the product as a soundless alarm clock – the bracelet vibrates when it’s time to wake up, without disturbing the significant other sleeping next to you.

Nyx Devices: Nyx is developing the Somnus Sleep Shirt, a tshirt with embedded respiration sensors. This is another approach for obtaining sleep analytics data and a significant step in the direction of wearable sensing. I am really looking forward to seeing the future of this product.

Blood Glucose Level

Dexcom Seven Plus Continuous Glucose Monitoring: This device is a continuous glucose monitoring system for people with diabetes. Unlike traditional glucose monitors that require a blood sample for each analysis, patients introduce a very small sensor/transmitter into their bodies that sends glucose level information to a receiver outside the body in real-time. The sensor is placed subcutaneously and continuously measures glucose levels in the interstitial fluid.

iBGStar Blood Glucose Meter: The iBGStar is the first available blood glucose meter that connects to the Apple iPhone. I fully expect to see more devices connecting and sending data to mobile devices, so this device is emblematic of this upcoming trend.

Remote Health Monitoring

GE QuietCare: This is a system for activity detection and recognition in the home. It’s the result of a partnership between Intel and GE. Data sent from the sensors is analyzed by algorithms to detect any out-of-the-ordinary events that may put residents at risk. We are just starting to see the category of health telematics and personal health monitoring emerge and this company is hoping to bring it to scale.

Others

Asthamapolis: A project whose goal is to track when an asthma inhaler is used. In connection with a mobile phone, a GPS-powered inhaler maps and track asthma symptoms/triggers, helping patients learn more about asthma while also improving public health. I anticipate an increasing number of medical devices associating its use with contextual information like time of day and location.

Affectiva Q-Sensor: Affectiva produces the Q Sensor, a wearable, wireless biosensor that measures emotional arousal via skin conductance, a form of electrodermal activity that grows higher during states such as excitement, attention or anxiety and lower during states such as boredom or relaxation. This is not a consumer product, but it’s rapidly becoming a popular tool in research studies where tracking emotional levels is required.

Duo Fertility Monitor: DuoFertility is a fertility monitor to help women get pregnant naturally, and avoid invasive medical procedures such as IVF. A patch-like sensor monitors body temperature and transmits it to a receiver, which then calculates a fertility level. As part of the service offered, fertility experts might also get involved to examine the data and provide feedback.

CellScope: An attachment that clips onto the back of an ordinary camera phone and turns it into a portable microscope capable of visualizing single-celled pathogens like malaria parasites or tuberculosis bacteria. This is a very powerful idea, especially in the context of developing nations.

Withings Scale and Blood Pressure: Withings is trying to make it easy to track basic physiological signals like blood pressure, BMI and weight over time. They have developed devices that transmit data wirelessly over WiFi or to a mobile phone with the goal of making it easy to get a daily health overview.

Medical/Clinical

Proteus Biomedical: Proteus develops ingestible event markers (IEMs), tiny, digestible sensors made from food ingredients, which are activated by stomach fluids after swallowing. Once activated, the IEM creates an ultra-low-power, private, digital signal detected by a microelectronic recorder configured as either a small bandage style skin-patch or a tiny device inserted under the skin. The detector date- and time-stamps, decodes, and records information such as type of drug, dose, and place of manufacture, and also measures and reports physiologic parameters such as heart rate, activity, and respiratory rate. Detector data can be combined at the server-level with other telemetered parameters such as blood pressure, weight, blood glucose, and patient-generated feedback. Quite remarkable.

Given Imagine: This company designed the PillCam video capsule for capsule endoscopy, a medical procedure which allows your physician to visualize parts of your gastrointestinal tract. The GI tract is a part of the digestive system and extends from the mouth to the anus. This is very impressive technology.

There’s no doubt we will see an explosion of human sensing devices over the next 5-10 years. What is available today is just scratching the surface of what is possible. Two major trends are already evident, (1) the mobile phone will become the hub to many of these sensors and devices (although I expect that we will see increasingly more sensors that are capable of transmitting data wirelessly over cell networks) and (2) we will see a lot of experimentation with non-invasive body sensing (e.g. tracking sodium and glucose using nano-sensors with a bio tattoo and a smart phone).

For additional resources related to this topic, I suggest the Quantified Self Guide to Self-Tracking. The guide lists applications and services beyond health and medicine sensing, but it contains lots of examples of human health sensing tools.

Quantified Self is a community of people including hackers, academics and others who are riding the wave of ubiquitous sensing and computing (e.g. mobile phones) to track various aspects of their lives. The focus of many of these tracking efforts centers on improving health, sometimes in the context of assessing health risks, sometimes encouraging reflection and behavior change, and sometimes with the goal of extending life. As the name suggests, quantification is a core element of the movement. Kevin Kelly and Gary Wolf, the duo who brought the community together and organized the event with many collaborators and volunteers, have been applying the Homebrew Computer Club model to the Quantified Self community and I think it’s working very well at the moment.

The conference was organized around talks in the main auditorium and breakout sessions spread out throughout the day covering a wide range of topics. The organizers planned the event well, and left plenty of time in-between sessions for attendees to chat, and for the more engaging sessions to keep going for an extra 30 minutes if necessary. Healthy lunches and snacks were provided.

Needless to say, I met large number of interesting people working on interesting projects such as David Duncan (journalist and author who’s been writing about the absolute state of the art in human testing, genomics, biotech and health innovation, usually undergoing the procedures he writes about as well), Greg LeMond (former professional bike racer, won the Tour de France three times and is interested in self-tracking for exercise performance, usually on the bike), Patricia Brennan (professor at the University of Wisconsin-Madison and leader of Project HealthDesign, an initiative that aims to bring observations of daily living into clinical care), Hugo Campos (who would like to hack his implanted defibrillator and have access to the data the device sends to Medtronic and his doctors over a phone line), Bill Jarrold (computer scientist working on language analytics for assessing brain health) and Tito (who is producing an open-source PCR machine).

There were many startups and established companies showcasing their products, some of which haven’t even been released yet. In general these startups fell into three categories, (1) activity tracking, (2) activity data aggregation, visualization and analysis and (3) health-oriented communities.

Under activity tracking were companies like Zeo, RunKeeper, BodyMedia, Phillips and GreenGoose, selling (or planning to sell) various types of devices for activity and physiological data tracking. I was particularly impressed by Zeo and the uptake they’ve got so far. In one of the talks, Zeo’s CEO or CTO mentioned that they currently have the largest repository of sleep data in the world, and a portion of it is made public and available to researchers. This makes sense, since sleep research is typically confined to research labs and the Zeo device allows anyone to collect sleep pattern data.

In the space of data aggregation, visualization and analysis were startups such as Sen.se, Quantter and Scanadu. I left the event with an intriguing impression of Scanadu. They plan to open their system so that users can input data from devices like Zeo and many others. At that point, Scanadu will help users understand the impact of their data and offer health recommendations. It’s an ambitious goal and to be seen whether they will succeed.

Finally, companies like MedHelp and Genomera are all about bringing people together. MedHelp is a portal for health-communities and Genomera is in the new space of personal science, offering an application that lets users join or initiate health studies. In my opinion, Genomera and some of the other companies in the field are behind of the most transformative trends that I am observing in what some people call Health 2.0. In the (really) long term, the bet that Genomera is making is that the future of health is crowdsourced and participatory, and increasingly less reliant on centralized government entities.

At a meta-level, I noticed a number of trends in this field. Now that I’ve had the chance to synthesize all the talks, conversations and interactions that I had, here are some of my observations:

The Future of Logging

Whether it’s the food you eat, your mood, your thoughts or your exercise, tracking activities and physiological signals is at the core of the quantified self movement. Not surprisingly, it was represented at the conference en masse, with speakers sharing logging techniques and startups showcasing tools that facilitate logging and visualization. In terms of actual sensing technology, I didn’t see anything tremendously novel. GreenGoose does seem to have a promising approach to tracking physical activities, but because the product is not available yet, they weren’t able to provide details. The team behind the upcoming Basis watch was attending the event, but again, we couldn’t see the product in its final form. We will certainly see new sensors and human tracking modalities next year. This area will keep evolving, without a doubt.

In my view, one of the key points has to do with the value and analysis of tracked data. I expect that in the years to come, the quantified self movement will evolve from not only tracking data but making sense of it and validating it as well. What kinds of patterns emerge from this data? How can these patterns be turned into best practices that transform them from merely interesting to indicators you can’t live without. In other words, sensing is fairly easy to do, analysis is much harder in comparison.

Health Policy, Urban Planning

At the moment the focus is on the individual. Are there any opportunities for tracking and aggregating activity data at the population level? Can you improve health policy at the neighborhood, city level when you collect this type of data for large numbers of people who are similarly geo-located? If city officials had access to public self-tracking data and could see that large numbers of people in a neighborhood are avid bikers, would that facilitate investment in bike lanes and similar infrastructure?

Personal Genomics

My impression was that at least half of the participants at the conference have gone through some type of genetic testing, 23andMe being the most popular service. By and large, the motivation for personal genetic testing is mostly at the curiosity stage at the moment, but I expect that it will become much more of a health tool as we move forward. Consequently, I imagine that in the years to come, we will see more personal applications that rely on genetic data. The same way that groups of people are experimenting with treatments for conditions today, personal science-style, we will see genetics coming into the mix as genetic-wide association studies (GWAS) become yet another tool available to citizen scientists. Also, in terms of GWAS, many of the studies center on genotype/phenotype correlations. I see an opportunity to introduce additional quantification to capture the (extended) phenotype, even in high-profile projects like George Church’s PGP. Without a doubt, there will be many more discussions around this topic in the future, as these projects and techniques evolve.

Personal (Social) Health Science

My grandmother is in her late eighties. She suffers from intermittent blackouts, usually when walking to church or the grocery store, that result in fainting and falls. She’s visited multiple doctors and undergone numerous tests, and all that medicine has been able to say is that her circulatory system is probably not as effective as it used to be and that under rigorous exercise, such as walking, her brain ends up not being oxygenated enough and turns itself off momentarily. What if my grandmother discovered, by experimentation or chance, that eating half a stick of butter a day made her blackouts go away? That would be tremendous for her well being. Now imagine if there was a way for her to share her discovery and improve the condition of hundreds, maybe thousands of people around the world that also suffer from blackouts.

Scenarios like this are starting to happen, even though most times we don’t understand the cause/effect health science behind them. The truth is, my grandmother doesn’t care whether an effective treatment for her condition comes from the Harvard Medical School, the FDA or from a neighbor next door. As long as it works, she can go to church and that’s all that matters. That’s the idea behind personal (social) health science, and I bet that we are not even at the tip of the iceberg yet. We can just barely see it under water.

Data Openness

As we begin to have more access to information about our health and our activities, through personal devices, the value of this data will become clear. Therefore we will demand more openness with regards to the legacy medical procedures that have traditionally kept medical data out of our hands. This in turn will accelerate the adoption of personal health records. Does it make sense for a private company like Medtronic to not give individuals access to their own heart analytics? The answer might be yes from a financial and legal stand point but Medtronic and others like it will begin to feel the heat to change the way they operate.

It is exciting to be part of a new field as it emerges, and that’s exactly what happened in earnest in Mountain View, despite the fact that Quantified Self Meetups have been taking place all over the world for a while now. The energy in the room was palpable. In his closing talk, Kevin Kelly discussed how quantification has all the ingredients to become part of the evolving scientific method. I can’t help but agree.

I’ve recently found out about Practice Fusion’s Medical Research Data set and have visited the site where the data is hosted a couple of times. It seems like an interesting data set, which was the basis of a competition, etc. However, now the ad critters that live within my browser have decided that I am interested in Practice Fusion and are showing me their ads no matter where I go on the web. There is certainly an ad network running behind the scenes, trying to do the contextually “smart” thing. But this example illustrates the naiveté of the web ad industry.