I am getting this error Missing number, treated as zero. > l.31 >{\arraybackslash}p{1.5in}}
My MWC is (Using springer aps journal class)
\documentclass[pdflatex, sn-aps]{sn-jnl}% American Physical Society (APS) Reference Style
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\begin{document}
%%%BIG TABLE BEGIN
\begin{sidewaystable}
\begin{center}
\begin{minipage}{\textheight}
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\caption{Comparative study on some Fall Detection Systems~[TB-Threshold and ML-Machine Learning based]}
\label{tab:1} % Give a unique label
\begin{tabular*}{>{\raggedright\arraybackslash}p{0.60in} % changed to tabular and first column
>{\raggedright\arraybackslash}p{0.85in}
>{\raggedright\arraybackslash}p{1.1in} % increase width
c
>{\raggedright\arraybackslash}p{0.65in}
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>{\raggedright\arraybackslash}p{1.5in}
>{\arraybackslash}p{1.5in}}
\toprule
\thead{Reference}&\thead{Sensor\\type}& \bfseries Sensor location&\thead{No. of\\ sensors}&\thead{Processing\\location}&\thead{Method}&\thead{Accuracy\\in \%}&\thead{Features}&\thead{Limitations}\\
\midrule\\
\cite{Wang2018}
& Accelerometer, Gyroscope
& External & $>1$ & On board & TB & 77.5
& Fine grained fall detection with good accuracy.
& No text based location, Fall and break of device aspect not considered.\\
\addlinespace
\cite{Abdulaziz2021}
& Triaxial Accelerometer
& External & 1 & On board and Remote
& TB+ML & 99.45
&Killer heuristic optimized AlexNet convolution neural network(KHANCN). Sensor information is initially collected by placing 6 sensors on 14 subjects.
&Fall location and time not available. No real life implementation case study. Fall and break aspect not considered.\\
\addlinespace
\cite{Tsinganos2017}
& Triaxial Accelerometer
& External & $>1$ & On board and remote
& ML & 91.83
&Fall detection and ADL based on KNN classifier\%
&Store \& analyse, no live data, device fall is not considered.\\
\addlinespace
\cite{Boudouane2019}
&Camera
& External & $>1$ & Remote
& ML & 68.33
& Image information is used for fall classification.
&Slow, multiple image capturing device may be required, privacy issues.\\
\addlinespace
\cite{Junior2018}
& Triaxial Accelerometer
& External & $>1$ & Remote
& TB + ML & Unknown
& Threshold analysis, reminder analysis and decision tree algorithm .
& The non-functional aspect of the device after a fall is not considered.\\
\addlinespace
\cite{Silva2018}
& Pressure Sensor
&Integrated in the operator's shoe & $>1$ & remote
& TB & 86
& Good result accuracy and can be implemented in IoT platform.
& The nature of walking surface has a direct impact on the accuracy.\\
\addlinespace
\cite{lee2018real}
& Accelerometer, Gyroscope
&Smartphone in chest pocket & $>1$ & On board and remote
& TB & 92.5
& Smartphone Google API (location), Good accuracy.
& Device location is not suitable for heart patient, Google API is not accurate in remote locations.\\
\addlinespace
\cite{Tong2013}
& MEMS tri-axis accelerometer
&Upper trunk of the body & 1 & Remote
& ML & 100
& Fall detection and prediction using hidden Markov chain.
& Location information as well as fall alike cases are not considered.\\
\addlinespace
\cite{Ruan2015}
& UHF-RFID
&Different locations inside the room & $>1$ & Remote
&TB + ML & 92.45
& Device and location independent fine grained fall detection.
& Not suitable for outdoor monitoring.\\
\addlinespace
Proposed system
& Smartphone accelerometer
&Gender and garment independent, easy to wear phone holder & 1 & Remote
&TB & 94.45
& \multicolumn{2}{p{\dimexpr1.5in+1.5in+2\tabcolsep+\arrayrulewidth}}{
Text based location + SMS, Indoor and outdoor monitoring, Ineffectual device consideration, Non ambulatory, Non self-recovery warning only so number of warnings are less, In real life, the system could reduce the FoF in the PD patients upto 10\%.} \\
\bottomrule
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\end{document}
The template can be Downloaded
\begin{tabular*}needs a width argument, for example\begin{tabular*}{\textwidth}