Our stroke researchers should be able to use this to objectively determine stroke gait problems. Then with that objective data we could map rehab protocols that fix those specific problems. But that won't occur, we don't have two neurons to rub together to create a spark of innovative thought in stroke.
Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions
- Anisoara Paraschiv-IonescuEmail author,
- Christopher Newman,
- Lena Carcreff,
- Corinna N. Gerber,
- Stephane Armand and
- Kamiar Aminian
Journal of NeuroEngineering and Rehabilitation201916:24
© The Author(s). 2019
- Received: 5 July 2018
- Accepted: 25 January 2019
- Published: 4 February 2019
Background
Physical therapy interventions
for ambulatory youth with cerebral palsy (CP) often focus on
activity-based strategies to promote functional mobility and
participation in physical activity. The use of activity monitors
validated for this population could help to design effective
personalized interventions by providing reliable outcome measures. The
objective of this study was to devise a single-sensor based algorithm
for locomotion and cadence detection, robust to atypical gait patterns
of children with CP in the real-life like monitoring conditions.
Methods
Study included 15 children
with CP, classified according to Gross Motor Function Classification
System (GMFCS) between levels I and III, and 11 age-matched typically
developing (TD). Six IMU devices were fixed on participant’s trunk
(chest and low back/L5), thighs, and shanks. IMUs on trunk were
independently used for development of algorithm, whereas the ensemble of
devices on lower limbs were used as reference system. Data was
collected according to a semi-structured protocol, and included typical
daily-life activities performed indoor and outdoor.
The algorithm was based on
detection of peaks associated to heel-strike events, identified from the
norm of trunk acceleration signals, and included several processing
stages such as peak enhancement and selection of the steps-related peaks
using heuristic decision rules. Cadence was estimated using time- and
frequency–domain approaches. Performance metrics were sensitivity,
specificity, precision, error, intra-class correlation coefficient, and
Bland-Altman analysis.
Results
According to GMFCS, CP children were classified as GMFCS I (n = 7), GMFCS II (n = 3) and GMFCS III (n = 5).
Mean values of sensitivity, specificity and precision for locomotion
detection ranged between 0.93–0.98, 0.92–0.97 and 0.86–0.98 for TD,
CP-GMFCS I and CP-GMFCS II-III groups, respectively.
Mean values of absolute error
for cadence estimation (steps/min) were similar for both methods, and
ranged between 0.51–0.88, 1.18–1.33 and 1.94–2.3 for TD, CP-GMFCS I and
CP-GMFCS II-III groups, respectively. The standard deviation was higher
in CP-GMFCS II-III group, the lower performances being explained by the
high variability of atypical gait patterns.
Conclusions
The algorithm demonstrated
good performance when applied to a wide range of gait patterns, from
normal to the pathological gait of highly affected children with CP
using walking aids.
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