dwellSense Visualizations

Anind Dey, Ph.D., dwellSense Principal Investigator, Carnegie Mellon University

During an earlier phase of our research, we identified the prospective information needs of our various stakeholders: elders, caregivers, occupational therapists and primary care physicians. In particular, our elders were quite interested in real-time or daily information about their performance of their everyday activities, but didn’t find a lot of value in long-term data or trends. They weren’t opposed to the long-term data, but they really wanted to have the daily information in order to see if they had taken their medication, and to see how well they were doing each day.

We designed the visualization below to run as an application on a Samsung Galaxy 10.1” tablet. The visualization has three horizontal panels, one for each of the ODLs that our sensing systems are tracking: medication taking, making phone calls and coffee-making. The large type size, the colors used and the simple design were purposefully chosen to make it easy for the elders to consume the information.

For medication tracking, we present information about morning and evening pills. In the visualization below, you can see that the subject took their morning medicine on time at 5:07 a.m., but had difficulty determining the right day of the week, having opened the Sunday and Monday doors before correctly opening the Tuesday door. The subject missed the evening pill completely. The two bar charts on the right indicate how timely the subject was in taking his or her medicine and how well the subject performed the task. Because the subject missed his or her evening medicine, the timeliness bar chart is red . The other chart is yellow to indicate his or her difficulty in selecting the correct pillbox door. These bar charts are intended to serve as quick additional abstractions to make it easy to consume the medicine-taking information.

For the phone calls, the visualization indicates the number of calls made and, of those, the number of times the user . The bar chart provides a summary of how well the user performed this task.

For coffee-making, the visualization indicates whether the subject made coffee correctly. In particular, it shows whether the subject performed each of four key coffee-making tasks correctly: getting water, adding the coffee, placing the carafe in the coffee maker and starting the machine. In this case, the subject performed all four tasks correctly, which is what the associated bar chart indicates.

 

We are still going to provide a long-term visualization to some of our subjects to see what value it can provide, despite their seeming lack of interest. This will be a more traditional line graph, with time on the horizontal axis, and the quality of the performance of the task on the vertical axis. Subjects will be able to click on any of the data points to receive the regular daily view for the chosen day. We expect that by providing both kinds of views, we can improve subjects’ awareness of their own performance, and that this will cause them to engage others in discussions (e.g., caregivers, occupational therapists, doctors) about their abilities, with the goal of improving their performance over time.

Learn more about the dwellSense project.
Read more posts from the dwellSense team.

 

Listening to Health in Everyday Living

Patricia Flatley Brennan, R.N., Ph.D., Project HealthDesign Director

About three years ago, our Project HealthDesign project teams began hearing something odd and unexpected from the patients involved in their user-centered design activities — the sound of health in everyday living! Health doesn’t come labeled in the professional, clinical-speak of signs and symptoms. It sounds like a family’s laughter, strained dinner conversations or the peaceful snoring of a parent who’s sick. It sounds like the birds one hears while taking an especially long walk or the raspy breathing that precedes an asthma attack. It sounds like the words an elderly woman uses when she explains to her physician that she “just doesn’t feel right” after taking her medication.

We’ve called the phenomena these sounds reflect “observations of daily living,” or ODLs. ODLs are sensations, behaviors, environmental exposures, thoughts and social experiences that serve as clues to health and health activities. We have been trying to better understand the stories they tell.

We’ve learned that with ODLs, there is a jargon or rhythm to the sound of health, and that it doesn’t sound at all like the sounds of health care. What it does sound like may be idiosyncratic, or unique for each patient. Like the more familiar signs and symptoms, ODLs provide snapshots into a person’s health situation. However, unlike signs and symptoms, which have a scientifically proven link to an underlying health process, ODLs represent what a patient pays attention to; this is why ODLs  are so important for patient-centered care.

Our five current Project HealthDesign project teams are hearing about a whole new set of ODLs. Listen to what they are hearing:

The Embedded Assessment team at Carnegie Mellon University is using sensors embedded in household objects such as coffee pots, telephones and pill boxes to monitor how well elders are doing everyday activities. Understanding how well — or how poorly — an elder performs routine tasks may provide the early warning signals needed to bring help in a timely fashion. The sensors, if fired in the anticipated sequence, will show that the person is handling things just fine. However, sensors firing out of sequence or not at all suggests that some type of cognitive disruption is occurring — perhaps the elder can’t remember the steps to making coffee, or has forgotten to take meds at a certain time. Of course, one misstep doesn’t equate to profound confusion. That’s why we think of ODLs as sentinels, not signals. The ODL here is the firing sensors; this ODL must be interpreted and validated in order to serve as an indicator for action.

The iN Touch team from San Francisco State University is reaching out to teens who are both obese and at risk for depression. This group is going to use camera-equipped cell phones to prompt the teens to take photos of their food, record their menus on the spot and to rate things like: feelings, interactions with others, and activity levels. The ODLs here might sound like nutrition, mood, social activity and using the phone to capture information in real time. This information will be stored and reviewed later with a health coach, and could serve as the key for real success in weight management.

Again, awareness of ODLs is only part of the story. The other part is looking for patterns in the clues. It’s a lot like the sound of a crying baby — crying is an ODL, but distinguishing between the sound of a distressed cry and a bored cry means the difference between responding with comfort or stimulation. And, speaking of babies crying, we’ve wondered whether ODLs exist if you can’t hear them or know what they mean. But of course they do!

The FitBaby team at the University of California, Irvine is working with parents and young infants. They’ll be looking at how things like the number of diaper changes or the sound of a mom singing to a baby give clues to the quality of the parent-child relationships and the health of the baby. They’ve created a Mind Garden to appraise parents’ stress levels, and are using photos of baby poop to evaluate the adequacy of baby’s nutrition.

Over the next few weeks we’ll be introducing you to more sounds of health in everyday living. Join us on the journey to decode these clues and figure out how to use them to improve health!

Read more posts about ODLs.

Learning to Work Both on the Technology and Clinical Side



By Anind K. Dey, Carnegie Mellon University, Embedded Assessment Principal Investigator

One of the challenges we are facing on our project is the attempt to conduct research across two different disciplines or domains and to demonstrate credible accomplishments in both. On one hand, there is the world of technical achievement in computer science, which myself and the members of my team are very comfortable with. We know what an appropriate contribution is in this field and what entails appropriate demonstrations of such contributions. On the other hand, is the focus on clinical outcomes, and is an area where we feel much less comfortable.

From our interactions with grantees from Round 1 of ProjectHealthDesign, it's clear that others share the same discomfort, although they may feel more comfortable on the clinical side and less so on the technical or user interface side.

Our goal for our project is to better understand how embedded sensing, a technical construct, can be used to help elders and related stakeholders reflect on sensed aspects of everyday behaviors to determine whether there is a change in an elder's cognitive health. We have chosen this goal specifically, as it matches our understanding of a technical contribution in our field of human-computer interaction.

In addition, a clinical goal which we will not achieve in our project would be to validate that the measures obtained from embedded sensing are as accurate, if not more accurate, than existing assessment techniques. While we would love to work toward this goal, it seems beyond our abilities with respect to the numbers of people we can work with, and the timeline of ProjectHealthDesign.

I would love to hear from my clinical colleagues and others about methods or approaches that would allow for both technical and clinical contributions. In this age of increased interest in applying technology to clinical domains, a better understanding of how to support both types of work simultaneously would enable many of us struggling on both sides of this problem. What are some approaches we should pursue?

Navigating the path to Meaningful Use goals.

By Tim Patton, Computation and Informatics in Biology and Medicine (CIBM) Trainee, Project HealthDesign NPO, University of Wisconsin-Madison

As we have posted (They can't do it alone - ONC needs everyone's input), we at Project HealthDesign want to make meaningful use really useful for patients.  At the same time, we want to facilitate communication between patients and physicians, nurses, public health workers and other clinicians.

ONC has stated that the goals of Stage 2 and Stage 3 are as follows: 

• By 2013, meaningful use of HIT should guide and support care processes and care coordination.
• By 2015, meaningful use of HIT should yield measurable improvements in quality, safety and outcome of care processes, including enhanced quality measures and extensive decision support at the individual and population level and enriched tools for patient self -management.

For Stage 2, it will be important to focus on the best use of resources.  Given the short time horizon, we would be best served to focus our efforts on:

• Generalized data structures that support physical activity, diet, and sleep. These are emerging as ubiquitous ODLs and represent the best value proposition as we begin to integrate support.
• Flexible tools that allow individuals to view the data as I see fit.  As we gradually come to understand the underlying ODLs, how we decide to view the underlying data will grow with our understanding. This might take the form of tools that will allow anyone who accesses that data to form a “view” that highlights the meaningful aspects while removing the noise.  Furthermore, if a lay person opts to share his/her view, others can understand the personal importance of ODLs (which may be very different from a professional’s view).

For Stage 3, it will be important to continue to build upon previous efforts to make ODLs more useful to everyone involved by:

•  Expanding support to other ODLs that fall outside the “big three” mentioned above.
•  Supporting disambiguation by combining multiple methods of capture (coding accordingly so others reviewing the data will know how it was captured).  For automated capture, calibration and algorithms can influence the data collected.  Self report suffers from a large array of influences.  To strengthen the information that can be derived from the data, it might be beneficial to combine collection methods (e.g., if automated collection agrees with self report, then we might increase our confidence that the data represents an accurate picture).
•  Recording the ambient context (reality mining) by connecting ODLs with other forms of data, such as GPS camera images, allergen counts, sound pressure levels, etc.  This might lead to new insights into how ODLs are related to contextual or environmental factors.
•  Creating observation standards that serve to identify the original source of the data, such as automated or self-report. This might play a role analogous to LOINC Codes, which can identify the specific lab test (with a set of standards and assumptions) that produced the value in the database.  As new devices and techniques come to market, they can be certified against such standards, thus insuring a certain level of consistency across vendors.

For ODLs to provide meaningful use, they must go beyond isolated data points and become truly connected to the full tapestry that describes personal health.

Sensing Technology Can Observe it All - Almost

By Anind K. Dey, Carnegie Mellon University, Embedded Assessment Principal Investigator.

In our project, we are focused on sensing activities of daily living from which we want to create measures of observations of daily living. One of the activities we are trying to sense is how well our subjects take their medicine. This involves almost every stage of the process:

• picking up the pillbox
• opening the appropriate slot for the current day of the week
• taking the pills out of the slot
• getting some water
• using the water

We are interested in the quality of the performance of each of these activities. However, it should be pretty obvious that the one thing missing from the list and that we can't sense without using invasive video (which our subjects would not accept) is whether the pills are actually consumed. In searching for a solution, we have discovered some very interesting technologies. While they aren’t available today, one day, in the not-too-distant future...

Proteus Biomedical makes smart pills that are powered by the stomach's gastric juices. Once powered, a digestible microchip embedded inside a pill reports out an id. The id is picked up by a microelectronic receiver that is worn as a skin patch or implanted under the skin. The receiver can then send the information out to a cell phone.

Researchers at the University of Florida have developed a similar system using pills with a microchip and antenna, and created the company eTect. The antenna receives energy from a transmitter worn, say on a wrist, and sends it's unique id, along with data collected from the microchip like the time and the type of pill that was ingested. The transmitter can then communicate that out to an external device for recording.

Researchers at Georgia Tech have developed MagneTrace, a necklace that measures the strength of the magnetic field in the neck area. In this system, tiny magnets are embedded in pills, and when a pill is swallowed and travels down the esophagus, the necklace measures the change in the magnetic field and determines which type of pill was swallowed and how many. The necklace then sends that information to a smart phone or nearby computer. An alternative form factor for the sensing device is a simple patch. It was 94% effective at correctly detecting the magnets in a lab evaluation.

Very cool technology, but they will all require, arguably small but time-consuming changes in the ways that pills are manufactured. In other words, it’s exciting to follow the advances, but they won’t be available for our work. What will we do? Unfortunately, we may just have to skip that aspect of pill taking, as we don't have a solid technology with which to track actual pill ingestion.

Is Every Hug, Every Fuss, Really an ODL?

By Patricia Flatley Brennan, Director, Project HealthDesign.

Benedict Carey's recent article in the New York Times describes a research project in which 32 families allowed videographers to tape, code, and categorize every waking moment for a week in their lives. Mindful of recent NYT articles on patient self-report and The Data-Driven Life and additional follow-on articles one might wonder, has the NYT gotten the observations of daily living message? Is continuous video recording the ultimate way to monitor ODLs? Or, is this ODLs on steroids?

We recognize that ODLs are an emerging concept and we are just beginning to get our hands and heads around them. We've learned that ODLs are uniquely defined key indicators or cues that lay people pay attention to in a way that helps them better understand what is happening to their health. Over the past few weeks our projects have introduced you to the some of the ODLs our grantee teams are working on - such as the mood of a teen as they drop by a fast food joint with their friends, or the nausea and pain that accompanies a flare-up of Crohn's disease.

What's important here are two things: (1) the cues are personally meaningful; (2) the importance of keeping track of them can go up and down for clinicians. We've begun to believe that conscious awareness of the ODLs by the person is an integral characteristic of ODLs. However, as Nikolai Kirienko pointed out, sometimes the person lacks the will or the strength to actively record these observations. External sensors, like motion detectors, pollen sensors and even videotapes complement person-initiated and controlled observation capture, and may round out the picture of what is happening to the patient and why.

So, should videotaping of people's lives be encouraged as a way to capture ODLs?  Probably not - at least videotapes don't provide the complete solution. Despite the ease and non-intrusiveness of passive capture approaches, such strategies provide only part of the picture, and may, because they lack the cognitive awareness, be less helpful in the long run because they don't actively engage the person in their own health.

There's no one solution to ODL-watching and we'll need everyone on the journey to help characterize personal health, figure out how to best monitor and share the indicators important to lay people and their clinicians, and re-think what PHRs can do about enhancing life. Stay tuned and share your thoughts.

Getting to ODLs

By Anind K. Dey, Carnegie Mellon University, Embedded Assessment Principal Investigator.

As part of our project to detect cognitive decline in elders, we are embedding sensors to observe how they perform everyday activities and how these performances change over time.

In recruiting participants for our project, we conduct interviews with potential subjects, observe and videotape how they perform the daily activities of interest (medicine taking, meal preparation, and telephone use), and discuss whether we can realistically build a customized solution for monitoring the performance of these activities. This is of course in addition to whether we think they would be a good subject (at risk for cognitive decline but is not currently experiencing decline, lives alone, performs the activities reliably ...).

In our attempt to recruit subjects, we have already faced two different types of setbacks: technology-related and people-related.

Technology
We have deployed instrumented pillboxes, bed and chair sensors, and telephone use sensors in the apartments of two pilot participants. We will be visiting participants every two weeks so technologies are designed to last at least that long. While replacing batteries, pillboxes, or sensors on a frequent basis might be a pain, we decided that designing technology that had to last longer wasn’t a good use of resources. So far the technology had been working great and after troubleshooting a few anomalies, we are receiving data on our server.

Our biggest struggle is customizing the technology to each individual’s habits. As you can imagine, elders perform activities in any number of ways. In our fieldwork, we observed some elders taking pills using a pillbox that they filled or had filled once a week, while others took their pills straight from the pill bottles multiple times a day. While both ways are perfectly acceptable, they each call for a unique sensing solution and from an engineering standpoint they are very different. This is just one example, but it applies to all the observations of daily living we are interested in, and all the facets of those daily activities as well: e.g., swallowing pills using bottled water vs. getting water from the tap, making instant coffee vs. using a coffeemaker ...

It has become clear that if we could recruit subjects that all performed their daily activities in the same way, our lives would be A LOT easier, but the systems we build would be less realistic, as this doesn't mimic real life behavior.

People
On the human side, we are lucky to have the support of Presbyterian SeniorCare and the Allegheny Country Area Agency on Aging to help us interview, assess, and finally recruit subjects that meet our stringent criteria. After interviewing and videotaping a woman making breakfast and taking pills we thought she would be a great match for the project. Unfortunately she passed away of natural causes before we could recruit her. It is easy to get caught up in the goals of our research and the design of the technology, but it is also important to remember that we are dealing with real people who have real issues and lives outside of our study. While this can be obvious to others, we sometimes need to be reminded of it.

In our attempt to get to real observations of daily living, we have some significant obstacles to overcome.

Are There ODLs in Diapers?

By Gillian Hayes, University of California, Irvine, FitBaby Principal Investigator.

The Project HealthDesign blog is supposed to offer a “behind the scenes” view of our research. Well, right now, in the FitBaby project we are working on trying to figure out whether—and how—we can sense anything and everything about babies pooping. Fifteen years ago when I first entered the field of computer science I never thought I’d be here.

Let’s back up a little bit. We have been interested in measuring the ambient gases in the home to try to make some connections between infant comfort, health, and wellbeing and any potential contaminants in the air. One of our team members, Sen Hirano, a graduate student at UCI has a passion for tinkering.  Sen can often be found in the lab late at night soldering together something or another. 

A few weeks ago Sen came in to my office and informed me that he was pretty sure he could sense “flatus” using the air sensors we had bought. I nearly fell out of my chair. Once the surprise wore off, I asked him to show me his sensor streams, visualize the various gases he was sensing on his computer, and get, well, a bit more “sciencey” about the whole thing.  It seemed that our methane sensor was spiking a bit at the expected times, which made sense given our limited knowledge of the human excrement system. So, we began to talk about the idea that we could sense baby poops and baby gas in the crib using just our tiny gas sensors.

A few days later, on Sunday morning, I got an instant message from Sen. I think the conversation speaks best for itself:
 
Sen: The major components of the flatus, which are odorless, by percentage are:
Nitrogen: 20–90%
Hydrogen: 0–50%
Carbon dioxide: 10–30%
Oxygen: 0–10%
Methane: 0–10%
Sen: Maybe I chose the wrong gas
Gillian: Nitrogen! Really? Can we sense that?
Sen: idk
Sen: I'm about to start looking
Sen: For example, in one study of the feces of nine adults, only five of the samples contained archaea capable of producing methane. Similar results are found in samples of gas obtained from within the rectum.
Sen: Hmmm
Gillian: Interesting
Sen: The thing is that it's still working?
Sen: But the lack of methane in some places might be troublesome
Sen: Though since these sensors are also sensitive to other gases it might be okay?
Gillian: Well that’s the real question I suppose.  What else are the sensors picking up?
Gillian: See if you can find out what gases comprise baby poop when breastfeeding... i suspect it might be different from those in bottle fed babies
Sen: Hmmm
Sen: This is turning into a biology experiment
Gillian: Totally
Sen: Found it
Sen: http://journals.lww.com/jpgn/Fulltext/2001/05000/Gas_Production_by_Feces_of_Infants.9.aspx
Gillian: NICE
Gillian: That’s totally the right thing
Gillian: And funny
Gillian: All at once
Sen: lol
Sen: "I can tell the difference between breast fed and formula fed babies"
Gillian: Do we need new sensors for this super power of yours?
Sen: ha ha
Sen: Maybe...

Since that Sunday, we have gotten really serious about this idea.  We are starting to think that the potential to monitor this kind of detail about the baby digestive system could be incredibly interesting both clinically and in terms of basic science and research.  Parents are often concerned about their infant’s constipation or gassiness, but when asked, they cannot always remember the last “poopy diaper” nor how many feedings were bottles as opposed to breast fed. The color of poop might be a warning sign of trouble to come if the infant is bottle fed but totally normal if breast fed. The very gas contents themselves might even tell us something clinically relevant. At this point, we just don’t know, and that is very exciting.

At the same time, we think there may be some interesting computer science problems in here. We are in the process of doing controlled experiments to test our sensors and algorithms. Sen and I are lucky enough to have Monica Tentori, a post-doc in my lab, and Don Patterson, a fellow professor down the hall, both around to advise on the machine learning aspects of this work.  We keep ordering new sensors, soldering together interesting components, and yes, we are prepared to collect “poopy diapers” in sealed Tupperware to test out their gas production capabilities.  It promises to be an exciting time in our lab… and hopefully not too smelly of one.

ODLs: Not as Simple as We Thought

By Anind K. Dey, Carnegie Mellon University, Embedded Assessment Principal Investigator.

Two weeks ago, I attended the second Project HealthDesign workshop at the Vanderbilt Center for Better Health, with the Project HealthDesign team and the other grantees. It was another valuable workshop. Clinicians from each of the grantee projects attended, and there was a wonderful presentation by Dr. Kevin Johnson from Vanderbilt, a first round grantee.I not only learned more about the projects of my fellow grantees, but I also learned a lot more about our Embedded Assessment project. Up until this point, I thought I understood the observations of daily living (ODLs) that our project wanted to focus on, but I realized that I had something to learn. My team's focus is on using sensors embedded into everyday objects that can be used to monitor how well an elder performs everyday activities, and therefore be used to perform in situ assessment of cognitive decline. In consultation with our occupational therapists on the team, Linda Kent from Presbyterian SeniorCare and Diane Collins from the University of Pittsburgh, my graduate student, Matthew Lee, and I came up with a list of ODLs: medicine taking, phone use and meal preparation. But, what we realized during conversations with the other attendees are that these ODLs are really meta-ODLs or observable ODLs (not a great name, I know!) or proxies. The ODLs that we really care about are the things that these ODLs consist of: change in fine and gross motor control, ability to initiate and sequence activities, ability to situate oneself, etc. I'll call these elemental ODLs, for lack of a better name. While we had been considering our meta-ODLs somewhat independently, I think we have an opportunity to look at them more holistically, and treat all sequencing actions (in meal preparation and medicine taking, for example) as the same set of activities.
 
This is particularly exciting because it could allow us to answer the "why" question that has alluded us up until now. When we show potential users of our technology sample graphs produced by the system, they may reveal an increase in how much time they spend on a particular task over a month we will inevitably get asked "Why is that?". We can't answer that question without ground truth data and observations of all activity inside their residences. Perhaps it's due to a cognitive decline, but perhaps it's because they always call a friend in the middle of the activity. One is clearly a cause for action, while the other is not. But, if we can now collect and visualize data about the meta-ODLS, we can see how patients perform on elemental ODLs within these. For example, we could show that a patient has difficulty with sequencing tasks, both for the medicine taking and the meal preparation tasks. These kind of correlated activities would go a long way at answering the "why" question and helping our patients and clinicians trust the data our system can produce.

Obviously we're really excited about this new understanding. It's going to be a lot more work to explore these ideas, but I think it'll be really fruitful in the end!

Using Sensors to Track Cognitive and Physical Decline

This post is the second in a series that will introduce you to our five grantee teams.

By Anind K. Dey, Carnegie Mellon University, Principal Investigator.

Here at Carnegie Mellon University in Pittsburgh, we have been working with people with Alzheimer's Disease for a few years, focusing on how to improve their quality of life and that of their familial caregivers'. A clear source of tension was episodic memory impairment or EMI. This was true for caregivers in having to repeatedly remind their loved one about a past event, and for people with Alzheimer's Disease in losing their sense of self from not being able to remember important events from their past. Our approach to addressing EMI was to capture lifelogging information (images, audio, sensors) of events they wanted to remember, and then to apply heuristics from our fieldwork to create salient summaries, to filter the vast amounts of information being collected down to a consumable and reviewable amount. This approach improved our subjects' confidence in their own memory and their ability to recall aspects of episodic memories, and reduced the burden on caregivers.

We are very excited about this approach of creating salient summaries, and for Project HealthDesign, we believe that we can apply this approach to detecting the onset of cognitive and physical decline, before an official diagnosis of an impairment such as Alzheimer's Disease. Often such a diagnosis is delayed due to a lack of recognition of change in one's abilities, and/or the extensive time between assessments. Earlier diagnosis would permit earlier interventions, both behavioral and pharmacological. 

Our project
We are planning to deploy sensing technologies that can detect changes in the performance of everyday activities, such as medicine taking, sleeping, using the phone, and meal preparation. As our sensors detect changes in the quality of performance of these different activities (e.g., time taken to complete the activity, performing steps correctly or out of order, skipping or adding steps), our proposed system will create appropriate salient summaries to present to the person whose activities are being sensed, occupational therapists, physicians and other care providers who can offer interventions as necessary. The biggest benefits of our technological approach are that it can be used to monitor activities 24-7 and detect changes continuously and doesn’t place users into "test situations" where they behave differently because someone is observing them. An example of one of our sensor systems is shown here: a pillbox instrumented to detect which door a user has opened, what is placed inside each door, whether the pillbox is being shaken or turned upside down, etc. For the medicine taking task, and the other tasks, we are sensing all the steps that an individual takes to perform the task, in a privacy-preserving and comprehensive manner, from selecting the appropriate pills to swallowing them with water.

The project team
We've put together a great team for this project. Matthew Lee, my student, is a 5th year Ph.D. student in the Human-Computer Interaction Institute, and is focusing on the use of technology and salient summaries to improve quality of life of elders. Diane Collins is a professor in the Department of Rehabilitation Science and Technology at the University of Pittsburgh, and is also a licensed occupational therapist with over 20 years of clinical experience. Linda Kent is a licensed occupational therapist with many years of clinical experience at Presbyterian SeniorCare, a local network of living and care options for elders and people with disabilities, and a key partner in this project.

We are very excited about our project and hope you'll visit our webpage for updates and follow our progress through the blog!