Wednesday, October 7, 2020 — 2:00 PM EDT

Rohan Shanbhag, VP of ML and AI Strategy, will be discussing how Achu Health has developed a personalized health and wellness platform driven by a proprietary machine learning algorithm on-device that uses passively collected biometric data from wearables to predict the onset of illness before symptoms appear. Achu Health aims to assess the scientific rigour of its technology to ensure that the evaluation can be backed by Health Science professionals in providing meaningful value to the end user.

About Achu Health

Achu Health, a health-tech AI startup founded in 2014 and based in Toronto, Canada, recognized the undeniable factors that play a role in the physical and mental health of individuals and consequently developed a mobile health platform aimed at making users aware of how specific trends in their passively collected biometric data can be indicative of illness onset. This aligns with their mission of promoting metricized health to invoke optimal living.

Originating on the Fitbit platform, Achu Health amassed 80,000 users organically which set the pace for exploring and developing an on-device machine learning solution.

Achu Health uses both objectively measured health data (e.g., heart rate, steps, sedentary time, sleep duration) from Apple Watch, subjectively assessed mental health data (e.g., stress, anxiety, tiredness) from prompted questions in the application, and environmental factors based on location (e.g., air temperature, air pressure, and humidity) to predict the onset of respiratory symptoms characterized by the common cold and flu before they appear completely on-device.

Registration is required.

 

Cost 
Free
Location 
ONLINE via Webex


,
Canada

S M T W T F S
25
26
27
28
29
30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
  1. 2021 (8)
    1. May (2)
    2. April (2)
    3. March (2)
    4. February (1)
    5. January (1)
  2. 2020 (31)
    1. December (2)
    2. November (3)
    3. October (4)
    4. September (3)
    5. August (1)
    6. July (3)
    7. June (6)
    8. May (2)
    9. April (1)
    10. March (3)
    11. February (1)
    12. January (2)
  3. 2019 (44)
  4. 2018 (31)
  5. 2017 (16)
  6. 2016 (28)
  7. 2015 (12)
  8. 2014 (11)
  9. 2013 (12)