Accurate, robust estimation of vertical, jump height from smartphone accelerometers
Reference persons TAIAN MARTINS
External reference persons Dr Alessio Gallina, School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham (UK)
Research Groups Laboratory for Engineering of the Neuromuscular System (LISiN)
Thesis type EXPERIMENTAL APPLIED
Description The height of vertical jumps can be computed from the time subjects take to land after take-off: that is, from the flight time. Recently, the market has been populated by apps developed for achieving this goal, based mainly on video registrations of the subject while jumping. This semi-automated procedure introduces a bias, which is likely critical due to the relatively low, frame rate of smartphone videos. Other apps compute flight time from the acceleration data provided by smartphones during vertical jumps, without providing information on how large the error in jump height estimation may be and on how flight time is computed. Seemingly, these apps assume the acceleration, as detected by the smartphone, is a proxy of the acceleration of the body centre of mass in the vertical direction. The position and attitude of the smartphone when jumping likely invalidates this assumption, which consequences remain unverified.
In this proposal, the student is expected to conduct experiments with the goal of addressing the question: can jump height be estimated accurately from the vertically-corrected, acceleration data provided by smartphones?
The activities proposed will be conducted in collaboration with Dr Alessio Gallina, School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham (UK).
Required skills Good understanding of neuromuscular physiology
Advanced Matlab (or other coding languages) skills
High English proficiency
Notes Students willing to undertake this proposal are requested to contact the supervisor and submit:
1. A letter of interest for the proposed project
2. Elements motivating the decision to pursue this thesis (5-10 lines of text)
Deadline 31/12/2023 PROPONI LA TUA CANDIDATURA