Related references
Note: Only part of the references are listed.Fall Classification by Machine Learning Using Mobile Phones
Mark V. Albert et al.
PLOS ONE (2012)
Using Mobile Phones for Activity Recognition in Parkinson’s Patients
Mark V. Albert et al.
Frontiers in Neurology (2012)
Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0
Melanie Swan
JOURNAL OF SENSOR AND ACTUATOR NETWORKS (2012)
Continuous monitoring and quantification of multiple parameters of daily physical activity in ambulatory Duchenne muscular dystrophy patients
Pierre-Yves Jeannet et al.
EUROPEAN JOURNAL OF PAEDIATRIC NEUROLOGY (2011)
Reliability and Validity of Bilateral Ankle Accelerometer Algorithms for Activity Recognition and Walking Speed After Stroke
Bruce H. Dobkin et al.
STROKE (2011)
Using Sensors to Measure Activity in People with Stroke
George D. Fulk et al.
TOPICS IN STROKE REHABILITATION (2011)
Activity identification using body-mounted sensors-a review of classification techniques
Stephen J. Preece et al.
PHYSIOLOGICAL MEASUREMENT (2009)
Parkinson's disease: clinical features and diagnosis
J. Jankovic
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY (2008)
Ambulatory monitoring of physical activities in patients with Parkinson's disease
Arash Salarian et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2007)
Effectiveness of automated locomotor training in patients with chronic incomplete spinal cord injury: A multicenter trial
M Wirz et al.
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2005)
Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement
MJ Mathie et al.
PHYSIOLOGICAL MEASUREMENT (2004)
Targeting recovery: Priorities of the spinal cord-injured population
KD Anderson
JOURNAL OF NEUROTRAUMA (2004)
Ambulatory system for human motion analysis using a kinematic sensor:: Monitoring of daily physical activity in the elderly
B Najafi et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2003)
Share Paper
