Notch Research
One of macronutrients fueling the company and the product development at Notch is the customer feedback. Customer feedback may come from many sources, but recently we were lucky to add a principally new one: peer-reviewed articles and published academic research.
Product reviews in tech and mainstream media provide the perspective on the the first contact with the product. App-store reviews are the source of feedback on user experience, usability and emotional design. Customer and tech support interactions are the lines into the daily life of the product. Peer-reviewed publications however shift the focus of attention away from the product itself and onto its applications, thus adding a whole new dimension of insights.
We often have a hard time guessing which domain the next research publications that mentions Notch may coming from. While studying the academic projects related to our products, we have been introduced to the work in ergonomics, healthcare, electrical engineering and interactive arts.
As a way to express our gratitude and excitement regarding the use of Notch in the academic environment we are introducing a Academic Reward Program. Anyone who shares the link to their publications on work involving Notch would receive a year of free Extended license and substantial discount towards new Notch Pioneer kits.
To provide deeper context for Notch Pioneer and highlight all the work done by the members of our platform from the academia, we will now feature the relevant publications in the “Papers” section on our site. It is now open with the following publications:
- A Software Toolbox to Improve Time-Efficiency and Reliability of an Observational Risk Assessment Method.
Lenzi S.E., Standoli C.E., Andreoni G., Perego P., Lopomo N.F. (2019) - Comparison Among Standard Method, Dedicated Toolbox and Kinematic-Based Approach in Assessing Risk of Developing Upper Limb Musculoskeletal Disorders.
Lenzi S.E., Standoli C.E., Andreoni G., Perego P., Lopomo N.F. (2019) - SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning
Mauldin, Taylor R. and Canby, Marc E. and Metsis, Vangelis and Ngu, Anne H. H. and Rivera, Coralys Cubero - The Movement Undercommons: Movement Analysis as Meaning Making in a Time of Global Migrations
Grisha Coleman, Brenda McCaffrey - Interfacing Gestural Data from Instrumentalists
Martin Jaroszewicz
