[ad_1]
From crowdsourcing new rare disease treatments to accelerating the work of urban planners, five University of Toronto startups have moved a step closer to economic viability after participating The Entrepreneurship Hatchery‘s virtual demo day 2021.
A total of 17 teams competed in the Hatchery’s NEST process, an experiential learning opportunity that instills and promotes an entrepreneurial mindset in the participating U of T students and faculty.
Over the summer, attendees met potential co-founders, developed their business plans, and connected with mentors who provided support in a variety of areas including market research, branding, and intellectual property protection. The program culminated on Demo Day, when teams presented their startup ideas to a jury, including entrepreneurs and investors – some of whom are former Hatchery participants themselves.
The five winning teams will share $ 80,000 in seed funding that will help their companies move through the next phase of their development.
“The hatchery demo day is my favorite way to start a new academic year,” says Chris Yip, Dean of the Faculty of Applied Sciences and Engineering that houses the Hatchery program. “I am consistently impressed by the creativity, professionalism and energy of these dynamic students and I look forward to seeing them grow in the years to come.
“On behalf of the faculty, we congratulate all the teams that participated this year, as well as Joseph Orozco and his entire team in the hatchery for making this possible. “
Here are this year’s winning teams:
Civvic – AI-enabled web platform for developers and urban planners
Civvic has developed a web-based platform to bring all the different information required by city planners together in one place. (Image courtesy of Civvic)
Planning a new urban development is complex. It requires gathering information about the historical, social, and economic characteristics of a particular place or neighborhood – as well as working with a wide range of stakeholders. Civvic aims to streamline the research process by bringing all of this information together on a single platform.
“Preparing for Demo Day was one of the most challenging and rewarding activities our team has ever experienced,†says CEO Lewis Walker, a former University College student who recently graduated from the Human Geography and Planning and Urban Studies faculties of the Faculty of Arts and Science at U of T.
“We found that there is often a gap between what we think is going on and what is actually happening on the field. The willingness to learn and the ability to turn on the go were critical to our team. “
Civvic plans to further develop its online platform by the end of 2021 and is looking for new members for its team.
In addition to Walker, Civvic includes the most recent U of T graduates Michelle Zhang (Urban research, peace, conflict and justice, human geography); David de Paiva (Urban studies, political science); Khaled Elemam (Bioinformatics and computational biology); Patrick Thang (Rotman Commerce, Rotman School of Management); and master’s student Ian Hwang (Geography and planning).
Fovea – Portable sensors for the blind
Using a series of 100 coin-sized vibration motors, Fovea aims to translate visual information into tactile signals for blind people (Image courtesy of Fovea).
Fovea aims to help blind people by converting visual information into tactile signals that are transmitted through a wearable vest.
Embedded in the vest is a series of 100 coin-sized motors – each of which is capable of vibrating based on input from a portable camera. The system can provide certain basic information when entering a room, including the number of objects and their approximate distance from the user.
“We offer an alternative to photonic vision, so that blind people can perceive their surroundings in a neuro-spatial manner, orientate themselves better and become more independentâ€, says Alaa Shamandy, a machine learning researcher at the University Health Network’s Peter Munk Cardiac Center and a member of the Fovea team. “With our non-invasive technology, we are working on a more accessible world.”
Shamandy says the team developed a rudimentary prototype of the device. They will use funding from The Hatchery to develop a second version and enable voluntary testing by blind people. They also plan to apply for pre-market approval with regulatory agencies such as the U.S. Food and Drug Administration and Health Canada.
“The Hatchery has been extremely helpful throughout our development – from pitching in front of respected mentors and investors every week, to perfecting our business models and cash flows, and everything in between. We are in a much better place than at the beginning. “
In addition to shamandy, fovea includes: Sai Spandana Chintapalli, a graduate student in biomedical engineering at the University of Pennsylvania; and Kevin Fan, an emergency Resident at Aventura Hospital & Medical Center in Miami. The company is also considering expanding its team in Toronto.
Nightingale AI – Enhancing Physiotherapy with Vision-Based AI Tools
Nightingale.ai remotely connects physical therapists and their patients and uses visual AI to analyze the patient’s progress. The aim is to reduce the cost of physiotherapy while improving results in the short and long term. (Photo courtesy Nightingale.ai)
Nightingale.ai is an online platform that can be used by physiotherapists and their patients who are rehabilitating after knee or hip replacement surgery. It uses visual artificial intelligence to detect and analyze the same parameters physical therapists look for during personal visits, including the appearance of the surgical incision, the patient’s walking, and the movement of a new joint.
Based on this information, the platform can recommend a treatment plan or make personal appointments if necessary. By facilitating more frequent interaction and sharing between physical therapists and their patients, Nightingale.ai can improve outcomes while reducing care costs. It also provides extensive data on recovery outcomes that can be used to further optimize care for future patients.
“As a group of clinicians, engineers and researchers who have worked in the physical therapy field for many years, we are very familiar with the problems patients and care providers encounter during the rehabilitation journey,” says Sameer Chunara, CEO of Nightingale.ai and experienced physical therapist and owner of a community clinic in Toronto.
“We were surrounded by a team of advisors who helped us focus on what was really important at this stage.”
Read more about Nightingale.ai at the Lawrence S. Bloomberg Faculty of Nursing
The team plans to use the funds received to expand its core team of engineers and to further develop and test its platform. They hope to have a beta version in the next six months.
In addition to Chunara, Nightingale.ai includes: Donovan Cooper, Head of Site Operations at Altum Health; Assistant Professor Charlene Chu (Lawrence S. Bloomberg Faculty of Nursing); Meng-Fen Tsai (PhD student in biomedical engineering) and Chao Bian (PhD student in biomedical engineering).
ParkinSense – Medical Monitoring System for People with Parkinson’s
ParkinSense is a medical monitoring system that uses wearables to provide detailed, real-time data on the symptoms of Parkinson’s disease. It can be used to objectively determine the effectiveness of treatment. (Image courtesy ParkinSense)
Parkinson’s disease is a neurological disorder that affects more than 100,000 people in Canada. A common symptom of the condition is tremors, involuntary tremors, or tremors in the hand, leg, or foot.
ParkinSense is developing a monitoring system that can provide continuous, real-time data on tremors that can speed treatment for Parkinson’s patients by enabling more effective interactions with doctors. It also offers a mobile application that can remind patients when it is time to take their medication and track the effectiveness of those medications over time.
“The beginning of our journey was very meaningful to us because we had like-minded, passionate people who wanted to see us successful,†says Carolina Gorodetsky, a pediatric neurologist and movement disorder specialist at the Hospital for Sick Children. “The funding will help us with our prototyping and voluntary testing plans so we can refine our product and bring it to market in the near future.”
Besides Gorodetsky, ParkinSense also contains Akshata Puranic (a master’s student at the U of T Institute for Aerospace Studies) and Christopher Lucasius (PhD student in electrical engineering and computer science).
Varient – Crowdsourced data on the effectiveness of treatments for rare diseases
People with rare diseases can turn to off-label use of existing drugs in search of effective treatment. However, this is often undocumented, so resulting data on whether the treatment is actually effective or not is lost.
Varient wants to change this through crowdsourcing. The team has built an online platform that can collect and aggregate anonymized data on treatment effectiveness for groups of people who all live with the same rare genetic disease or disease. The goal is to take the guesswork out of the process and point the way to drugs that are most likely to be effective.
“Typically, rare disease patients rely on word of mouth to learn more about helpful off-label drugs,” says Katheron Intson, PhD student in pharmacology and toxicology at Temerty Medical School. “We can quantify the success of proven treatments and become a dynamic information provider for these populations.”
The team plans to use the funding to conduct extensive testing of its Alpha product, with the goal of launching in 2022. Intson says the hatchery is a valuable bridge between technology and business.
“I’ve been a scientist my entire professional life and the rest of my team are software developers,†she says. “The business aspect of starting a business was a real blind spot for us. The Hatchery has provided guidelines that have helped us redefine where we focus our energies and efforts. “
In addition to Intson, Varient includes: Chen Zong Lu (Computer science); Zuoqi Wang (Computer science); Jingyi sun (Computer science), Shukui Chen (Applied Mathematics); Yexiong Xu (Computer science); and Siyang Liu (Computer science).
[ad_2]
