The multidisciplinary research of the Laboratory of Movement Analysis and Measurement aims on improving movement monitoring and analysis in real-world conditions, through the design of wearable systems and algorithms. We are particularly interested to characterise sport performances and pathologies affecting motor function.
18th August 2020 – Frontiers in Bioengineering and Biotechnology article out – Sensor fusion approach for estimation of instantaneous sprint velocity
Salil Apte at LMAM, in collaboration with industry, designed a method to estimate the instantaneous velocity in sprinting using a sensor fusion approach, by combining the signals from wearable Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) sensors. Results demonstrated that the method was valid to compute an accurate velocity profile with respect to a Doppler radar and it could compensate for and improve upon the accuracy of the individual IMU and GNSS velocities. The study was published online in the journal Frontiers in Bioengineering and Biotechnology.
August 6, 2020 – Public thesis presentation of Pritish Chakravarty
Pritish Chakravarty had successfully defended his thesis: “Sensor and the Beast: Generalised Methods to Recognise Animal Behaviour and Quantify Energy Expenditure Using Inertial Sensors, and Applications”. The thesis supported by SNSF was in collaboration with University of Zurich.
February 11, 2020 – Lancet Neurology journal article out Long-term unsupervised mobility assessment in movement disorders.
This Personal View paper (co-authored by Atash Atrsaei PhD student at LMAM) highlights the potential of wearable-based unsupervised monitoring to provide more ecologically and meaningful information to patients than the supervised data acquired in the traditional clinical setting, which in turn can provide researchers with more accurate, granular, and disease-specific measures.
February 7, 2020 – Public presentation of Matteo Mancuso
Matteo Mancuso had his public defense of his thesis: “Evaluation and robotic simulation of the glenohumeral joint”. Th thesis supported by SNSF and LORF was in collaboration with CHUV-DAL.