## 5 Conclusions  
This study classified normal and stroke gait patterns by using time- and frequency-domain features obtained from acceleration data provided by an IMU sensor placed on the subject's lower back. Two stages of feature selection, which included statistical analysis and _SNR_ calculation, were used to reduce the selected features until only four features remained. Among four SVM kernels, the quadratic kernel showed the highest accuracy when evaluated through cross-validation; this kernel and the cubic kernel both showed the highest accuracy (96.55%) when different test datasets were used. This study proved that a single placement of an accelerometer on a trunk was able to differentiate between normal  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors/Year** & **Subjects** & **Sensor Placement** & **Features and Classifier** & **Results** \\ \hline [PERSON] et al., 2009 [40] & 74 hemiplegic patients & Only 3D motion capture and no wearable sensors & * & Kinematics feature from 3D motion capture & 100\% and 86\% accuracy from kinematic feature \\ [PERSON] et al., 2016 [12] & 15 post-stroke, 17 Huntington’s disease, 10 healthy elderly & 3 IMU sensors placed on both shank and lumbar spine & * & Group specific HMMs and time- and frequency-domain features & 90.5\% \\ [PERSON] et al., 2018 [10] & 20 subjects of post-stroke and other neurological disorder & Multiple placement: 7 IMU on lower back, both thigh, shank, and foot & * & Time-domain feature and gait temporal parameter & 89.13\% testing accuracy from shank-based sensor and DT model \\ [PERSON] et al., 2018 [11] & 27 healthy control and 27 PD with different stages & 8 IMU placed on lower limbs and trunk & * & Spatiotemporal gait and kinematic parameters & 96\% when using all IMU and majority voting classifiers \\ [PERSON] et al., 2020 [13] & 8 peripheral neuropathy, 15 PD, 13 post-stroke, and 13 healthy control & 2 IMU placed on shank & * & 8 gait parameters SVM Classifier & 93.9\% \\ Ours & 11 healthy control and 12 post-stroke & 1 IMU placed on lower back (L5) & * & Time- and frequency-domain features SVM with 2 stages feature selection & 96.55\% \\ HMM, Hidden Markov Model. PD, [PERSON]’s disease. MLP, Multi-Layer-Perceptron. DT, Decision Tree. RF, Random Forest. LDA, Linear-Discriminant Analysis. K-NN, K-Nearest Neighbor. & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of method and results with other studies.  
and stroke gait patterns with the help of time- and frequency-domain features and an SVM classifier.  
Conceptualization, W.-C.H.; data curation, T.S., Y.-J.L., F.-C.Y. and K.-N.C.; formal analysis, T.S.; funding acquisition, W.-C.H., D.-Y.H. and K.-N.C.; investigation, Y.-J.L.; methodology, T.S.; project administration, W.-C.H.; resources, W.-C.H.; software, T.S. and C.-T.S.; supervision, Y.-Y.L., F.-C.Y., D.-Y.H., C.-T.S. and K.-N.C.; writing--original draft, T.S.; writing--review and editing, W.-C.H., Y.-Y.L. and Y.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research supported and funded by Tri-Service General Hospital with project code: TSGH-D-109032; and Tri-Service-General Hospital & National Taiwan University of Science and Technology grant number: TSGH-NTUST-106-03.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Antai-Tian-Sheng Memorial Hospital (TSMH IRB 16-079-B1).  
Written informed consent was obtained from all subjects involved in the study.  
The authors would like to thank all subjects who participated on this study. The authors would like to thank the graduate student who helped during subject recruitment and data collection process. The authors would also like to thank for the funding No. TSGH-D-109032 and TSGH-NTUST-106-03.  
The authors declare there is no conflict of interest on this study.