Innovation Pathways and Early College Data Reporting Fall 2021
Innovation for Tomorrow: What the future holds Fall...
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Fall detection using signals from real-world fall events
Innovation for Tomorrow: What the future holds
1Robert-Bosch Hospital Stuttgart, Germany 2University of Bologna, Italy 3Norwegian University of Science and Technology, Trondheim, Norway
Jochen Klenk1, Luca Palmerini2, Alan Bourke3, Lars Schwickert1, Lorenzo Chiari2, Clemens Becker1
Fall consequences – long lying
■ Less than 20% of all falls are observed by others
■ >10% long lying situations
■ Association between lying duration and consequences:
28% mortality
62% hospital admissions
62% of survivors were unable to live independently
Gurley RJ et al. Persons Found in Their Homes Helpless or Dead. New England Journal of Medicine. 1996 Jun 27;334(26):1710–6.
Automatic fall detection
■ Focus on body-worn sensor technology including accelerometers and gyroscopes:
Cheap, small & light weight
Mobile (indoor & outdoor)
Good patient compliance
Performance of published fall detection algorithms
Bagalà F et al. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE. 2012;7(5):e37062.
Common features used for fall detection
■ Impact detection
Acceleration sum vector magnitude (total, dynamic)
Maximal vertical velocity
Maximum jerk
■ Change of posture
Critical trunk inclination
Angular velocity
Simulated vs. real-world falls
* P < 0.05 (exact Wilcoxon test)
Klenk J et al. Comparison of acceleration signals of simulated and real-world backward falls.
Med Eng Phys. April 2011;33(3):368–73.
The FARSEEING meta-database
Subject
characteristics
- age
- gender
- disease
- function
- …
Technical
characteristics
- type of sensor
- sample rate
- sensor site
- duration
- …
Fall
characteristics
- date & time
- fall direction
- verification
- outcome
- …
Real-world fall meta-database
Fall signals
- accelerometer
- gyroscope
- magnetometer
Klenk J, Chiari L, Helbostad JL, Zijlstra W, Aminian K, et al. Development of a standard fall data
format for signals from body-worn sensors. Z Gerontol Geriat. 2013 Dec 1;46(8):720–6.
n = 208
Verified fall signals
Real-world fall example
vertical
medio-lateral
anterior-posterior
Device: Samsung Galaxy S3
Sample rate: 100 Hz
Range: ±20 m/s²
While pushing the door opener
falling backwards
Self-recovery
Kinematic parameters of real-world falls
Bourke AK, Klenk J, Schwickert L, et al. Temporal and kinematic variables for real-world falls harvested from lumbar sensors
in the elderly population. IEEE EMBC 2015.
Pattern recognition – the wavelet approach
Palmerini L, Bagala F, Zanetti A, Klenk J, Becker C, Capello A. A wavelet-based approach to fall detection. Sensors 2015
Common patterns – the wavelet approach
AUC = 0.98 (0.96-0.99)
95% sensitivity, 97% specificity
50% sensitivity, 99.99% specificity
Palmerini L et al. Unpublished 2016
Orientation estimation
“When washing the hair, towel slipped over the forehead and covered the eyes. Due to loss of vision, subject fell backwards.”
Madgwick SOH, et al. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR). 2011. p. 1–7.
Example of unique fall patterns
“Lost balance while bending down to pick up an object from the floor, then falling forward to the right side.”
Future directions
■ Combining different fall detection approaches
■ Setting- and disease-specific algorithms
■ Self-learning approaches (normal patterns)
■ Detection of recovery movements