At Rise of AI 2019 in Berlin, Monishwaran Maheswaran, a 12th-grade student who describes himself as a computer scientist, inventor and mathematician, presents two self-built deep learning projects aimed at healthcare. He traces his own path from teaching himself programming languages as a child to building an English-vocabulary app, an early chatbot, and, after encountering an accident involving a visually impaired person, a camera-and-earphone device that detects hazards such as crossroads and potholes and describes them in natural language. He argues deep learning's biggest impact will be in healthcare because of the volume of data the industry produces, and presents Project Health, a wristband that tracks tremor activity to detect Parkinson's disease years before symptoms appear, and Project Life, which analyzes smartphone behavioral data such as sleep, steps and web searches to detect emotional states linked to suicide risk. He states his own aim is to provide affordable, universal healthcare. A Q&A follows about funding, data sources and ethics approval for this kind of work.
Key takeaways
- Maheswaran built a camera-and-earphone device that detects hazards like crossroads and potholes for visually impaired users after learning of an accident.
- He argues healthcare will see deep learning's biggest impact because of the scale of data the industry produces.
- His Project Health uses a wearable tremor-tracking band to detect Parkinson's disease years before visible symptoms, with over 92% accuracy.
- His Project Life analyzes phone behavioral data such as sleep, steps and web searches to flag suicide risk and alert family members.
- He says social projects like his do not require large capital, relying instead on mentors and institutional collaborations such as with NIMHANS in Bangalore.
Detailed notes
From childhood tinkering to a device for the visually impaired
Maheswaran says he started opening up home appliances around age three, learned three programming languages by around age eight and built an application that measurably improved his English test scores, and was introduced to machine learning around age 11 or 12, when he built a simple chatbot that could solve basic mathematical problems. He says his view of why AI matters changed in tenth grade after he learned of an accident involving a visually impaired person; noting that most visually impaired people rely on rudimentary aids such as guide sticks or guide dogs, he built a device combining a camera and an earphone that scans the environment for dangers like crossroads, potholes or traffic signals and communicates them to the user in natural language. He also mentions collaborating with MIT on content for an EdX course on computational thinking and data science, and working on a mathematics research project at Harvard in 11th grade.
Why deep learning, and why healthcare
Maheswaran describes deep learning as loosely inspired by how the human mind processes information and as especially effective at finding patterns and cause-and-effect relationships in data. He argues its largest impact will be in healthcare because of the sheer volume of data the industry generates, citing a figure of about 153 billion gigabytes produced in 2013, growing at roughly 48% a year, which he projects would reach about 2,314 billion gigabytes by 2020. He illustrates the value of larger datasets with a number sequence example, showing that more data points make it possible to correctly identify a pattern, in this case the Fibonacci-like sequence 1, 2, 3, 5, 8, that a shorter sequence would not reveal.
Project Health: detecting Parkinson's years before symptoms
Maheswaran describes building Project Health after his grandfather died following a late diagnosis of Parkinson's disease. He notes that neurological disorders like Parkinson's typically progress for ten to twelve years before symptoms such as hand tremors become noticeable, and that early tremors are often mistaken for something else, such as too much coffee. His device is a wearable arm band that measures tremor activity; as the disease progresses, tremor intensity increases, creating a measurable cause-and-effect relationship that he says the system can track with an accuracy above 92%, and that can also indicate whether a prescribed medication is working if tremors fail to subside.
Project Life: behavioral data and suicide risk
Maheswaran turns to mental health, citing close to 800,000 suicides a year worldwide and stating that by the end of his talk about 22 people would have died by suicide; he describes suicide as the second leading cause of death for people aged 15 to 29. He argues that unlike diseases with visible external symptoms, depression and suicide risk are hard to detect because a person may not outwardly show distress, and existing supports such as hotlines and mental health workers are limited in availability, cost and hours, while social stigma discourages people from seeking help. His response, Project Life, is a phone application that tracks behavioral data such as sleep patterns, step counts, calories burned and web searches, looking for correlations and sudden changes, for example a person who normally sleeps regularly and stops sleeping after losing a job, or web searches for methods of self-harm, in order to flag risk early and alert family members or loved ones.
Q&A highlights
Asked how socially oriented projects like his are funded, Maheswaran says they do not require large capital; he gathers information from mentors around the world and collaborates with institutions, citing a partnership with NIMHANS, a university hospital in Bangalore, where he interviewed patients about how to improve their care. Asked whether he uses social media data, he says he currently does not, both because platforms restrict access to user data through their APIs and because users may be reluctant to grant permissions; his current data points are sleep, calorie burn, step count and web searches. Asked about ethics committee approval and informed consent, typically required for healthcare research, he clarifies that his approach does not involve directly interviewing or monitoring diagnosed patients, but instead analyzes everyday behavioral patterns of people who have not sought help, developed in consultation with a university hospital's psychology department.
Quotes
Machines and humans are not two independent identities. We are codependent and we have co-evolved. Technology is not the problem, rather technology is the solution.
Monishwaran Maheswaran
Questions answered in this talk
How can a wearable device detect Parkinson's disease early?
According to Monishwaran Maheswaran at Rise of AI 2019, his Project Health uses an arm band that measures tremor activity; because Parkinson's symptoms typically appear ten to twelve years after disease onset, tracking tremor changes over time can flag the disease before it is otherwise noticed, with an accuracy he states as above 92%.
How can smartphone data help predict suicide risk?
Monishwaran Maheswaran explained at Rise of AI 2019 that his Project Life tracks behavioral data such as sleep patterns, step counts, calorie burn and web searches for sudden changes or correlations, such as a normally regular sleeper losing sleep after a job loss, in order to flag emotional distress and suicide risk and alert family or loved ones.
Why is depression harder to detect than physical illness, according to this talk?
At Rise of AI 2019, Monishwaran Maheswaran said depression and other mental health conditions often show no obvious external symptom, unlike a visible illness such as a common cold, and that people hiding distress combined with social stigma make it especially hard for others, and even for humans generally, to recognize these patterns.