# Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

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## Abstract

**:**

## 1. Introduction

- Human activity recognition using motion sensor data of smartphone.
- A hybrid feature selection model combination of SFFS based filter approach and SVM-based wrapper approach.
- An effective human activity identification using multiclass support vector machine.

## 2. Literature Review

## 3. Proposed Model

#### 3.1. Activity Data Collection

_{i}= (x

_{i}, y

_{i}, z

_{i}) and gyro

_{i}= (x

_{i}, y

_{i}, z

_{i}), where i = (1, 2, 3, …, n).

#### 3.2. Heterogenious Statistical Feature Extraction

#### 3.3. Feature Selection

Algorithm 1 |

Input: the set of all features $Y=\left\{{y}_{1},\text{}{y}_{2},\text{}\dots ,{Y}_{n}\right\}$ Output: a subset of features $X=\left\{\text{}{x}_{j}\right|\text{}j=1,2,3,\dots ,\text{}k;\text{}{x}_{j}\in Y\}$ Where, $k=\left(0,1,2,\dots ,n\right)$ Steps: 1. ${Y}_{0}=\{\varnothing \}$ 2. Select the best Feature X ^{+}Update: ${Y}_{K+1}={Y}_{K}+{X}^{+};=+1$ 3. Select the best Feature X ^{−}4. If $J\left({Y}_{K}-{X}^{-}\right)>J\left({Y}_{K}\right)$ [$\left(X\right)$ = Criterion func.] Then, ${Y}_{K+1}={Y}_{K}-{X}^{-};K=K+1$ go to step 3. |

#### 3.4. Objective Function Based on Discriminant Feature

#### 3.5. Acitivity Recognition for Validation Purpose

## 4. Experimental Results

_{x}f

_{7}, A

_{x}f

_{15}, A

_{y}f

_{13}, A

_{z}f

_{1}, G

_{x}f

_{14}, G

_{z}f

_{13}} produces the best result with an average accuracy of 96.81%, whereas accuracy without feature selection is 90.84%. Figure 9 depicts the accuracy of individual activity with feature selectiona and without feature selectio. In [31], the researchers used neural networks and random forests to detect human activities. In previous research, accuracy rate was below than 95% for different activities on average. Table 4 shows the confusion matrix of classification using optimal feature set. Figure 10 present the classification performance of activities using proposed feature selection model.

## 5. Additional Experimental

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 3.**Examples of exceptions in feature distribution. (

**a**) Classes are well-separated; (

**b**) Classes are overlapped.

**Figure 7.**Sample accelerometer data (

**a**,

**c**,

**e**,

**g**,

**i**,

**k**) and gyroscope data (

**b**,

**d**,

**f**,

**h**,

**j**,

**l**) for different activities: (

**a**,

**c**) Stand to sit activity, (

**e**,

**g**) sitting activity, (

**i**,

**k**) walking activity, (

**b**,

**d**) stand to lie, (

**f**,

**h**) laying activity, (

**j**,

**l**) sit to lie activity.

**Figure 8.**Accuracy of different feature sets produced by support vector machine in wrapper method. Highest accuracy is mark in red circle: (

**a**) Accelerometer X-axial feature set; (

**b**) accelerometer Y-axial feature set; (

**c**) accelerometer Z-axial feature set; (

**d**) gyroscope X-axial feature set; (

**e**) gyroscope Y-axial feature set; (

**f**) gyroscope Z-axial feature set.

Features | Equation | Features | Equation |
---|---|---|---|

Mean | $\mu =\frac{1}{N}$$\sum}_{i=1}^{N}{x}_{i$ | Root Mean Square | ${\mathrm{x}}_{\mathrm{rms}}=\sqrt{\frac{1}{\mathrm{N}}\left({\mathrm{x}}_{1}^{2}+{\mathrm{x}}_{2}^{2}\dots +{\mathrm{x}}_{\mathrm{n}}^{2}\right)}$ |

Standard Deviation | $\mathrm{s}=\sqrt{\frac{1}{\mathrm{N}-1}{\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{N}}{\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^{2}}$ | Energy | $E={\displaystyle \sum}_{i=1}^{N}{\left|{x}_{i}\right|}^{2}$ |

Median | $M=\left(\frac{\frac{n}{2}-cf}{f}\right)\left(w\right)+{L}_{m}$ | SRA | $SF=\mu \left(\sqrt[2]{ab{s}_{X}}\right)$ |

Maximum | MAX(M) | Peak to Peak | $PPV=MAX\left(M\right)-MIN\left(M\right)$ |

Minimum | MIN(M) | Crest Factor | $c=\frac{\left|{x}_{peak}\right|}{{x}_{rms}}$$=\frac{\left|\left|x\right|\right|\infty}{\left|\right|x|{|}_{2}}$ |

Interquartile Range | $IRQ=\frac{3}{4\left(n+1\right)}thterm\text{}$$-$$\frac{1}{4\left(n+1\right)}thterm$ | Impulse Factor | $i=\frac{{x}_{peak}}{{x}_{mean}}$ |

Correlation coefficient | $r=\frac{n\left({{\displaystyle \sum}}^{\text{}}xy\right)-\left({{\displaystyle \sum}}^{\text{}}x\right)\left({{\displaystyle \sum}}^{\text{}}y\right)}{\left[n{{\displaystyle \sum}}^{\text{}}{x}^{2}-{\left({{\displaystyle \sum}}^{\text{}}x\right)}^{2}\right]\left[n{{\displaystyle \sum}}^{\text{}}{y}^{2}-{\left({{\displaystyle \sum}}^{\text{}}y\right)}^{2}\right]}$ | Margin Factor | $MF={x}_{peak}/{x}_{sra}$ |

Skewness | $SV=\frac{1}{N}{\displaystyle \sum}_{I-1}^{N}{(\frac{{x}_{i-}x}{\sigma})}^{3}$ | Shape Factor | $SF={X}_{rms}/\mu \left(ab{s}_{X}\right)$ |

Kurtosis | $\mathrm{KV}=\frac{1}{\mathrm{N}}{\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{N}}{\left(\frac{{\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}}{\mathsf{\sigma}}\right)}^{4}$ | Frequency Center | $FC=\sqrt{{f}_{1}{f}_{2}}$ |

Cross Correlation | ${\rho}_{i,j}=\frac{{X}_{i,j}}{\sqrt{{\sigma}_{i}^{2}{\sigma}_{j}^{2}}}$ | RMS Frequency | $RM{S}_{fr}=\mu (\sqrt{\left({\mathrm{fr}}_{\mathrm{x}}{}_{1}^{2}+{\mathrm{fr}}_{\mathrm{x}}{}_{2}^{2}\dots +{\mathrm{fr}}_{\mathrm{x}}{}_{\mathrm{n}}^{2}\right)})$ |

Absolute Mean value | ${A}_{M}=\mu \left(ab{s}_{x}\right)$ | Root Variant Frequency | ${\sigma}_{y}^{2}\left(M,T,\tau \right)=\frac{1}{M-1}\left\{{\displaystyle \sum}_{i-0}^{M-1}{y}_{i}^{-2}-\frac{1}{M}{[{\displaystyle \sum}_{I-0}^{M-1}{y}_{i}]}^{2}\right\}$ |

Variance | ${\sigma}^{2}=\left[{{\displaystyle \sum}}^{\text{}}{\left(x-\mu \right)}^{2}\right]/N$ |

Acc-x | Acc-y | Acc-z | Gyro-x | Gyro-y | Gyro-z | ||
---|---|---|---|---|---|---|---|

1 | Mean | A_{x} f_{1} | A_{y} f_{1} | Az f_{1} | G_{x} f_{1} | G_{y} f_{1} | G_{z} f_{1} |

2 | Standard Deviation | A_{x} f_{2} | A_{y} f_{2} | Az f_{2} | G_{x} f_{2} | G_{y} f_{2} | G_{z} f_{2} |

3 | Median | A_{x} f_{3} | A_{y} f_{3} | A_{z} f_{3} | G_{x} f_{3} | G_{y} f_{3} | G_{z} f_{3} |

4 | Maximum | A_{x} f_{4} | A_{y} f_{4} | A_{z} f_{4} | G_{x} f_{4} | G_{y} f_{4} | G_{z} f_{4} |

5 | Minimum | A_{x} f_{5} | A_{y} f_{5} | A_{z} f_{5} | G_{x} f_{5} | G_{y} f_{5} | G_{z} f_{5} |

6 | Interquartile Range | A_{x} f_{6} | A_{y} f_{6} | A_{z} f_{6} | G_{x} f_{6} | G_{y} f_{6} | G_{z} f_{6} |

7 | Correlation coefficient | A_{x} f_{7} | A_{y} f_{7} | A_{z} f_{7} | G_{x} f_{7} | G_{y} f_{7} | Gz f_{7} |

8 | Skewness | A_{x} f_{8} | A_{y} f_{8} | A_{z} f_{8} | G_{x} f_{8} | G_{y} f_{8} | G_{z} f_{8} |

9 | Kurtosis | A_{x} f_{9} | A_{y} f_{9} | A_{z} f_{9} | G_{x} f_{9} | G_{y} f_{9} | G_{z} f_{9} |

10 | Cross Correlation | A_{x} f_{10} | A_{y} f_{10} | A_{z} f_{10} | G_{x} f_{10} | G_{y} f_{10} | G_{z} f_{10} |

11 | Mean Absolute Value | A_{x} f_{11} | A_{y} f_{11} | A_{z} f_{11} | G_{x} f_{11} | G_{y} f_{11} | G_{z} f_{11} |

12 | Variance | A_{x} f_{12} | A_{y} f_{12} | A_{z} f_{12} | G_{x} f_{12} | G_{y} f_{12} | G_{z} f_{12} |

13 | Root Mean Square | A_{x} f_{13} | A_{y} f_{13} | A_{z} f_{13} | G_{x} f_{13} | G_{y} f_{13} | G_{z} f_{13} |

14 | Energy | A_{x} f_{14} | A_{y} f_{14} | A_{z} f_{14} | G_{x} f_{14} | G_{y} f_{14} | G_{z} f_{14} |

15 | SRA | A_{x} f_{15} | A_{y} f_{15} | A_{z} f_{15} | G_{x} f_{15} | G_{y} f_{15} | G_{z} f_{15} |

16 | Peak to Peak | A_{x} f_{16} | A_{y} f_{16} | A_{z} f_{16} | G_{x} f_{16} | G_{y} f_{16} | G_{z} f_{16} |

17 | Crest Factor | A_{x} f_{17} | A_{y} f_{17} | A_{z} f_{17} | G_{x} f_{17} | G_{y} f_{17} | G_{z} f_{17} |

18 | Impulse Factor | A_{x} f_{18} | A_{y} f_{18} | A_{z} f_{18} | G_{x} f_{18} | G_{y} f_{18} | G_{z} f_{18} |

19 | Margin Factor | A_{x} f_{19} | A_{y} f_{19} | A_{z} f_{19} | G_{x} f_{19} | G_{y} f_{19} | G_{z} f_{19} |

20 | Shape Factor | A_{x} f_{20} | A_{y} f_{20} | A_{z} f_{20} | G_{x} f_{20} | G_{y} f_{20} | G_{z} f_{20} |

21 | Frequency Center | A_{x} f_{21} | A_{y} f_{21} | A_{z} f_{21} | G_{x} f_{21} | G_{y} f_{21} | G_{z} f_{21} |

22 | RMS Frequency | A_{x} f_{22} | A_{y} f_{22} | A_{z} f_{22} | G_{x} f_{22} | G_{y} f_{22} | G_{z} f_{22} |

23 | Root Variant Frequency | A_{x} f_{23} | A_{y} f_{23} | A_{z} f_{23} | G_{x} f_{23} | G_{y} f_{23} | G_{z} f_{23} |

**Table 3.**Overall classification accuracy of final optimal features in wrapper approach in hybrid feature selection.

Accelerometer Axial | Gyroscope Axial | |||||
---|---|---|---|---|---|---|

_{x} | _{y} | _{z} | _{x} | _{y} | _{z} | |

Best Optimal feature | f_{a_x_2} = {A_{x} f_{2,} A_{x} f_{7,} A_{x} f_{9,} A_{x} f_{13,} A_{x} f_{15,} A_{x} f_{21}} | f_{a_y_9} = {A_{y} f_{2,} A_{y} f_{8,} A_{y} f_{9,} A_{y} f_{13,} A_{y} f_{15,} A_{y} f_{22}} | f_{a_z_5} = {A_{z} f_{1,} A_{z} f_{2,} A_{z} f_{7,} A_{z} f_{8,} A_{z} f_{14,} A_{z} f_{15,} A_{z} f_{21}} | f_{g_x_4} = {G_{x} f_{2,} G_{x} f_{3,} G_{x} f_{7,} G_{x} f_{8,} G_{x} f_{13,} G_{x} f_{14,} G_{x} f_{21,} G_{x} f_{22}} | f_{g_y_7} = {G_{y} f_{2,} G_{y} f_{8,} G_{y} f_{9,} G_{y} f_{14,} G_{y} f_{15,} G_{y} f_{17,} G_{y} f_{22}} | f_{g_z_1} = {G_{z} f_{2,} G_{z} f_{7,} G_{z} f_{9,} G_{z} f_{13,} G_{z} f_{14,} G_{z} f_{15,} G_{z} f_{21}} |

Overall accuracy | 94.15% | 92.8% | 92.25% | 93.15% | 90.1% | 92.15% |

Predicted Class | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Walking | Walking Downstairs | Walking Upstairs | Standing | Sitting | Lying | Stand-to-Sit | Sit-to-Stand | Sit-to-Lie | Lie-to-Sit | Stand-to-Lie | Lie-to-Stand | Recall | ||

Actual class | walking | 189 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 97.93% | |

walking downstairs | 2 | 259 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 1 | 0 | 97.00% | |

walking upstairs | 5 | 5 | 252 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 95.45% | |

standing | 0 | 0 | 0 | 178 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 98.34% | |

sitting | 0 | 0 | 0 | 3 | 175 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 98.31% | |

lying | 0 | 0 | 0 | 3 | 0 | 180 | 0 | 0 | 0 | 0 | 2 | 0 | 97.30% | |

stand-to-sit | 1 | 0 | 1 | 0 | 0 | 0 | 90 | 0 | 0 | 0 | 2 | 0 | 95.74% | |

sit-to-stand | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 89 | 3 | 0 | 0 | 0 | 95.70% | |

sit-to-lie | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 90 | 0 | 2 | 0 | 96.77% | |

lie-to-sit | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 90 | 0 | 3 | 94.74% | |

stand-to-lie | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 87 | 0 | 96.67% | |

lie-to-stand | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 87 | 97.75% | |

Precision | 95.94% | 97.37% | 98.82% | 96.74% | 97.77% | 98.90% | 95.74% | 93.68% | 95.74% | 97.83% | 92.55% | 96.67% | 96.81% |

Predicted Class | ||||||||
---|---|---|---|---|---|---|---|---|

Walking | Walking Downstairs | Walking Upstairs | Standing | Sitting | Lying | Recall | ||

Actual class | walking | 491 | 2 | 3 | 0 | 0 | 0 | 98.99% |

walking downstairs | 4 | 413 | 3 | 0 | 0 | 0 | 98.33% | |

walking upstairs | 11 | 1 | 458 | 0 | 0 | 1 | 97.24% | |

standing | 0 | 0 | 1 | 517 | 14 | 0 | 97.18% | |

sitting | 0 | 0 | 0 | 11 | 480 | 0 | 97.76% | |

lying | 0 | 0 | 0 | 4 | 0 | 533 | 99.26% | |

Precision | 97.04% | 99.28% | 98.49% | 97.18% | 97.17% | 99.81% | 98.13% |

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Ahmed, N.; Rafiq, J.I.; Islam, M.R. Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model. *Sensors* **2020**, *20*, 317.
https://doi.org/10.3390/s20010317

**AMA Style**

Ahmed N, Rafiq JI, Islam MR. Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model. *Sensors*. 2020; 20(1):317.
https://doi.org/10.3390/s20010317

**Chicago/Turabian Style**

Ahmed, Nadeem, Jahir Ibna Rafiq, and Md Rashedul Islam. 2020. "Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model" *Sensors* 20, no. 1: 317.
https://doi.org/10.3390/s20010317