Luca Palmerini


  • Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review
  • Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls
  • Physical Activity Classification for Elderly People in Free-Living Conditions
  • Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease
  • Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification
  • Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges
  • Machine Learning Based Fall Detector With a Sensorized Tip
  • LPAD-based fall risk assessment
  • Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.
  • Feature selection for the instrumented timed up and go in Parkinson's disease
  • Can fall risk be measured?,Il rischio di caduta può essere misurato?
  • Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study
  • Feature selection for accelerometer-based posture analysis in Parkinsons disease
  • Balance testing with inertial sensors in patients with parkinson's disease: Assessment of motor subtypes
  • A rule-based framework for risk assessment in the health domain
  • Hilbert-huang-based tremor removal to assess postural properties from accelerometers
  • Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test
  • Physical activity classification using body-worn inertial sensors in a multi-sensor setup
  • Quantification of motor impairment in Parkinson's disease using an instrumented timed up and go test
  • Classification of early-mild subjects with Parkinson's disease by using sensor-based measures of posture, gait, and transitions
  • Automatic identification of motor patterns leading to freezing of gait in Parkinson's disease: An exploratory study
  • A wavelet-based approach to fall detection
  • The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
  • Identification of characteristic motor patterns preceding freezing of gait in Parkinson's disease using wearable sensors
  • Technical validation of real-world monitoring of gait: A multicentric observational study
  • A clinical application of feature selection: Quantitative evaluation of the locomotor function
  • Fall risk assessment tools for elderly living in the community: Can we do better?
  • FRAT-up, a web-based fall-risk assessment tool for elderly people living in the community
  • A probabilistic model to investigate the properties of prognostic tools for falls
  • FRAT-up, a rule-based system evaluating fall risk in the elderly
  • Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization
  • Real-world gait detection using a wrist-worn inertial sensor: validation and comparison with the lower-back position. (Preprint)
  • Real-World Balance Assessment While Standing for Fall Prediction in Older Adults
  • On‐Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial
  • Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study (Preprint)
  • Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

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