This is a guest post by Sean Lorenz, the Founder & CEO of SENTER, a Boston-based startup using sensors and data science to support healthcare in the home. Sean explains how techniques from computational neuroscience can help make the smart home smarter and describes the speech recognition hurdles developers have to overcome to realize smart home potential.
Consumer IoT pundits rave about the “smart home,” where our lights, shades, sprinklers and coffeemakers do what we want them to do automatically as they learn about our behaviors and habits. But the fact is that our homes are still far from being smart. Manufacturers have focused primarily on enabling existing products to send and receive data to/from a customer’s mobile phone. Much of this work is outsourced to services teams with expertise in full stack web and mobile app development; they’re great at whipping up dashboards and control buttons, but not at solving the problems that matter most to consumers.
Today, the smart home lacks three critical ingredients that hinder widespread consumer adoption:
- Lack of protocol agreement, with increasing local protocol alliances.
- Lack of intelligence. IFTTT is great for early adopter techies, but my cookie-baking 62-year-old midwestern mom is never going to create a rule to combine her Philips Hue lights and SmartThings motion sensors to perform an automated action. Like…never ever ever.
- Lack of user experience. Home automation software seems to be stuck in the era of Wham!, Duck Hunt, and power-dressing with shoulder pads. Apple announced their own HomeKit app to give IoT iPhone developers a hand, but we’re a long way from usability.
The upshot? Data scientists have an important role to play in taking the home from being connected to being smart. I believe this will result from creating context-aware, speech-based applications that combine, and make better use of, data streaming in from sensors across potentially dozens of connected products in the home.
At SENTER, we are tackling one lobe in the smart home brain – health. As the US transitions from fee-for-service to value-based care, health care management is migrating from the hospital to our homes. We all know that a few charts and graphs telling patients how many steps they took today isn’t enough to reduce hospitalizations or predict flare ups in chronic illness.
But magic happens when we combine data from multiple sensors to create a user experience that makes managing health easier and more natural. At Senter we knew that a simple, rules-based system wouldn’t work for predicting an individual’s unique health concerns. Traditional machine learning approaches weren’t working well either. The biggest data science problem we faced was dealing with feature stacking across numerous time series streams.
In the rest of this post, I’ll dive deeper into the data science problems we’re working on to make a smart home health system work: 1) learnable sensor fusion algorithms and 2) better voice-based intelligent assistant applications.
As a computational neuroscience PhD student, I devoured papers on multimodal sensor integration in the mammalian brain. There’s a very special part of the brain called the posterior parietal cortex (PPC) whose job is to bind together inputs from across the sensory and motor areas to create higher level cognitive decision-making and planning. Modeling this area of the brain is extremely nonlinear and very hard to do (see my sad attempt here).
An old-school brain functions diagram
What does this have to do with the smart home? IoT needs to tackle the same problem, only with non-biological sensors. The goal of sensor fusion is to combine data from various sensor inputs to make smarter decisions.
Consider the example of predicting sepsis, a very serious condition among the elderly. Some key symptoms are fever, shaking chills, very low body temperature, decreased urination, rapid pulse and breathing rate, and vomiting. With smart home tools, we could use a connected bed mat to track body temperature, shaking or chills motion, and breathing rate; motion sensors to track times entered the bathroom; urine detection sensors in the toilet; and a wrist wearable to track heart rate. With the data collected, how would we fuse all these sensors to predict septic events?
There are different data science methods that are well-equipped for time series analysis. I’ve looked into recurrent neural networks with LSTM (see my IoT Slam talk and Ajit Jaokar’s work for reference). Another popular method, used by the Google self-driving car team, is Bayesian inference (see here). Alexandre Pouget and his research team even suggested that the brain uses a form of Bayesian inference to integrate and make sense of all this sensory input data. That said, there is plenty of preprocessing that goes on before it even reaches sensor fusion…but that’s a topic for another time!
How voice-based interaction systems need to evolve
Just predicting that a person is septic is not enough. We probably want to let them know! Part two of making the smart home actually smart requires seamless user interaction to improve algorithmic performance over time. So the next question becomes, what’s the best way to get users to engage with the systems and make their smart homes smarter? I believe voice-based devices and intelligent assistants like the Amazon Echo or Google Home will soon be the predominant site of user interaction, overtaking smartphones or tablets.
Imagine you’re a homeowner who just contracted a developer to build an AWS application that streams real-time IoT products to manage and reduce home energy usage. Your developer starts by creating a lambda function on sensor fusion algorithms to automatically adjust lights and shades, turn off outlets, and change room temperatures to keep the electric bill low. If her algorithms open the shades at times you don’t like, you need a way to correct that behavior, to tell the application to adjust its network weights. You could certainly tune weights with a prompt in a smartphone app, but it’s far more natural to say “Alexa, please raise my blinds back up.” An Alexa custom skill can then relay this feedback up the chain to the AWS application so it can update its behavior.
While this may sound good in theory, is it actually possible? Yes and no. At SENTER we’ve found that people (particularly elderly patients) absolutely love the idea of using voice-based devices for user experience. Returning to our sepsis example, we can now ask seniors qualitative questions about how they’re feeling to strengthen confidence scores. But a number of UX and interaction issues still need to be solved before systems like Amazon Echo can really take off in smart home applications.
The biggest issue with voice-based interfaces (and Amazon Echo in particular) is two-way interaction. There is currently no way for a developer to program Echo to ask homeowners unprompted questions (but developers frequently request this from the Alexa team). Let’s say we want to use the motion sensors to trigger when someone is in the same room as the Echo so that we can ask them the occasional health-related question or ask “Did you just fall, Mrs. Jones? Should I call for help?” Denny Britz’s excellent vision of conversational interfaces with machines in the home will have to wait a little longer.
Equally problematic is how these devices process human language. It’s a very hard problem to build a bot that can process a statement it hasn’t seen before, making inferences like we do in daily conversations. Indeed, there are frustratingly many responses to the simple question “How are you feeling today?” When building an Echo app today, developers must provide a list of sample utterances for how a user might respond, which hinders the ability to continually learn. Deep learning may advance flexibility in the future, but we have work to do. Amazon’s got a healthy head start, and Viv, Apple and Google are following suite.
Lastly, the combination of smart homes and voice-based interfaces need stronger use cases (beyond knowing my IoT toothbrush brush count or having my refrigerator tweet when I need milk). I’ve spoken to hundreds of device manufacturers, investors, homeowners and IoT conference attendees over the years, and can confidently say that people don’t want a smartphone app for every connected product they buy. They want it all to just work together. In one simple user experience. And most importantly — they want their smart home to manage typical functions like energy, safety, lighting or health.
Intelligent, semi-supervised sensor fusion coupled with natural communication via a speech-based assistant in the home will get us there. Alexa, please Google “sensor fusion papers”. Let’s get to work.
- Sean Lorenz
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