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Table 2 Relevant features for calculating the LDA-value

From: A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm

Task 1

Features

Sensors

G1, p-value

G2, p-value

A1, p-value

A2, p-value

M1, p-value

M2, p-value

MAV

(a)

(f)

    

MAVFD

(b)

(g)

(k)

  

(x)

MAVSD

      

RMS

      

Peak

(c)

 

(l)

  

(y)

ZC

(d)

   

(s)

 

FMean

(e)

(h)

(m)

   

FPeak

 

(i)

 

(o)

(t)

 

F50

   

(p)

  

F80

   

(q)

  

Power3.5–7.5

  

(n)

 

(u)

(z)

EnAp

   

(r)

(v)

(A)

EnFuzzy

      

VAR

    

(w)

 

RANGE

      

INTQ

      

SKEWNESS

 

(j)

    

KURTOSIS

      

Task 2

Features

Sensors

G1, p-value

G2, p-value

A1, p-value

A2, p-value

M1, p-value

M2, p-value

MAV

      

MAVFD

      

MAVSD

      

RMS

      

Peak

      

ZC

(B)

(E)

(I)

   

FMean

 

(F)

    

FPeak

(C)

  

(K)

  

F50

 

(G)

(J)

(L)

  

F80

      

Power3.5–7.5

      

EnAp

 

(H)

    

EnFuzzy

      

VAR

      

RANGE

      

INTQ

      

SKEWNESS

(D)

  

(M)

  

KURTOSIS

      

Task 3

 

Sensors

G1, p-value

G2, p-value

A1, p-value

A2, p-value

M1, p-value

M2, p-value

MAV

      

MAVFD

      

MAVSD

      

RMS

      

Peak

      

ZC

  

(O)

 

(S)

 

FMean

      

FPeak

 

(N)

    

F50

      

F80

      

Power3.5–7.5

      

EnAp

  

(P)

  

(T)

EnFuzzy

      

VAR

      

RANGE

      

INTQ

      

SKEWNESS

   

(Q)

  

KURTOSIS

     

(U)

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