Editorial Feature

Analysis of Different Human Sitting Behaviors Using Inertial Sensor

Most people today sit for long hours with computers leading to unhealthy conditions like shoulder pain, headache, numbness, etc. The article discusses research revealing the use of an inertial sensor built into smartphones to overcome unhealthy sitting behaviors of the office worker.

Image Credit: suriya yapin/Shutterstock.com

Individuals nowadays spend most of their time sitting and sleeping without giving much care to sitting postures. Sitting is not a problem, however sitting in the wrong posture for a longer time on a daily basis can lead to major health issues like back pain, sciatica, and cervical spondylosis.

Conventional methods to analyze patient sitting postures involve letting a patient sit on the chair of the hospital in front of an observer like a therapist or doctor, completing QAs regarding sitting postures. These investigations replaced this with a smart cushion system that combines pressure sensors and IMU to monitor the sitting behavior at the workplace, in the car, and in a wheelchair.

Sitting posture monitoring systems (SPMSs) are also employed to evaluate the real-time postures of the individual and enhance the sitting behaviors. To enhance the analysis of sitting postures, it is necessary to replace the conventional techniques and think of sophisticated techniques. The research analyzed five various sitting postures, as shown in Figure 1.

A framework of the proposed system.

Figure 1. A framework of the proposed system. Image Credit: Sinha, et al., 2021.

Smartphones feature in daily life, containing inbuilt inertial sensors. These consist of a gyroscope, accelerometer, and magnetometer. Researchers track the body's movement with an IMU sensor attached to the upper rear trunk to track the exact movement of the spine, which in turn helps identify body postures.

Table 1 lists some of the major related articles discussed based on used sensors, classifiers, and their accuracy. 

Table 1. Literature review on the basis of previous papers. Source: Sinha, et al., 2021.

S. No Authors Type of
Classifiers Accuracy (%)   Limitations
1 Xu, Wenyao
et al. [4]
Textile sensor array in a smart cushion chair Naïve Bayes network 85.90 The recognition rate is less.
2 Roh, Jongryun et al. [6] Low-cost load cells (P0236-I42) SVM using RBF kernel 97.20 No. of subjects used is less, and power consumption is more.
3 Taieb-Maimon, Meirav et al. [12] Webcam, Rapid Upper Limb Assessment (RULA) tool. Sliced inverse regression 86.0 Analyzed only three symptom scales as back symptoms, arm symptoms, and neck pain severity.
4 Arif, Muhammad
et al. [33]
Colibri wireless IMU kNN 97.90 Dataset tested is small, and the optimal set of sensors need to be placed at the appropriate locations on the body.
5 Zdemir et al. [34] The MTw sensor unit, MTw software development kit Random forest 90.90 Cost is high, and the convergence time is more.
6 Rosero-Montalvo et al. [18] Ultrasonic sensor, pressure sensor, Arduinonano, LiPobattery kNN 75.0 Accuracy reported is much less.
7 Benocci et al. [35] FSR, digital magnetometer, accelerometer kNN 92.70 The number of subjects used in the experiment is less.
8 Shumei
et al. [36]
HTC smartphone
kNN 92.70 A posture-aware reminder system can be attached.

Framework of Smartphone-Based Sitting Detection

In the approach discussed, the inertial sensor of the smartphone is employed as a sitting behaviors detector. The research also prioritized data access, cost, compatibility, unobtrusive use, and system deployment.

The system gathered data for the five different static movements of the body while sitting in the chair (Figure 1). The smartphone was attached to the rear upper trunk at second thoracic vertebrae T2 to gather the measurable dataset (Figure 2). The system can identify the postures when the subject moves from the correct sitting posture to incorrect sitting postures.

Wearable sensor location in the human body.

Figure 2. Wearable sensor location in the human body. Image Credit: Sinha, et al., 2021.

Data Collection and Preprocessing

The study created a new dataset for analyzing human sitting behavior while sitting on a chair. A total of five general movements were taken into consideration—Left movement, Right movement, Front movement, Back movement, Straight movement.

The activities were carried out by the people under the supervision of an instructor to generate the dataset effectively. Table 2 shows the total number of instances for each activity, and the same is analyzed with a pie chart in Figure 3.

Pie chart representation for a number of instances.

Figure 3. Pie chart representation for a number of instances. Image Credit: Sinha, et al., 2021.

Table 2. Number of instances per activity. Source: Sinha, et al., 2021.

S. No Physical Activities No. of Instances Time (in Seconds)
1 A1: Left movement 35,565 712
2 A2: Right movement 37,757 756
3 A3: Front movement 33,268 665
4 A4: Back movement 29,460 590
5 A5: Straight movement 27,451 549
  Total 163,501 3272

Hardware Platform

Researchers used the One Plus 6 smartphone as a hardware platform for dataset gathering. Data pre-processing is a vital step after data collection and before feature extraction. 

Feature Extraction

The feature vectors are calculated from the collected dataset. The total acceleration of the accelerometer, gyroscope, and magnetometer are also calculated.

Morphological Features

The morphological features comprise the study of the morphological features like structure and shape from the dataset of sitting behaviors.

Entropy-Based Features

The article introduces two kinds of entropy-based features—wavelet entropy, the measure of relative energies in various signals, and is employed to determine the degree of the disorder and log energy entropy (LEE).

Feature Subset Selection

In this study, the researchers employed a filter method called correlation-based feature selection (CFS) as a feature selection algorithm. Table 3 shows the subset of features depending on their contributions.

Table 3. Selected features with contribution ratings. Source: Sinha, et al., 2021.

S. No Selected Features
1 Total-acceleration
2 Total-magnetometer
3 Y-accelerometer-MAV
4 X-gyroscope-MAV
5 Y-gyroscope-MAV
6 Y-magnetometer-MAV
7 X-accelerometer-HM
8 X-gyroscope-HM
9 Y-accelerometer-Var
10 Z-accelerometer-Var
11 Z-magnetometer-Var
12 X-gyroscope-SD
13 Y-gyroscope-SD
14 X-magnetometer-SD    
15 Z-magnetometer-SD
16 Z-accelerometer-skewness
17 X-gyroscope-skewness
18 Y-gyroscope-skewness
19 Z-gyroscope-skewness
20 Y-magnetometer-skewness
21 Y-accelerometer-LEE
22 Y-magnetometer-LEE
23 X-gyroscope-SSI
24 X-accelerometer-WE
25 X-gyroscope-WE
26 X-magnetometer-WE
27 Y-magnetometer-WE


The scientists combined the CFS technique and particle swarm optimization (PSO) search technique, a total of 27 features were selected out of 85 calculated features (Table 3).

Sitting Behavior Recognition Techniques

This article used three classifier techniques and compared them to the most popular recognition techniques like support vector machine (SVM), K-nearest neighbor (KNN), and naive Bayes after feature selection algorithm to recognize the sitting behaviors.

Results and Discussion

MATLAB R2021a was employed to carry out all calculations and analyses of the dataset of human sitting behaviors of office workers in the office environment.

Performance Analysis of Classifiers with Feature Selection of Accelerometer, Gyroscope, and Magnetometer

The performance evaluation of the KNN, naive Bayes, and SVM classifiers for feature selection with a gyroscope, accelerometer, and magnetometer is illustrated in Table 4. This table also depicts the comparison of the performance of each classifier for all five sitting behaviors.

Table 4. Classifier results with feature selection of accelerometer, gyroscope, and magnetometer. Source: Sinha, et al., 2021.

S. No Activities KNN
(K = 3)
(K = 5)
(K = 7)
(K = 11)
SVM Naive
1 A1: Left movement 99.20 99.91 99.91 99.92 99.99 98.51
2 A2: Right movement 99.97 99.97 99.97 99.95 99.98 99.06
3 A3: Front movement 99.96 99.96 99.96 99.94 99.98 99.15
4 A4: Back movement 99.67 99.72 99.70 99.68 99.76 91.89
5 A5: Straight movement 99.89 99.58 98.31 97.68 99.77 98.71


And Table 5 shows the confusion matrix of all five sitting behaviors by the SVM classifier.

Table 5. Confusion matrix of SVM classifier of selected features of accelerometer, gyroscope, and magnetometer. Source: Sinha, et al., 2021

  S. No Activity A1 A2 A3 A4 A5
True Class 1 A1 10,669 0 0 0 1
2 A2 1 11,476 0 0 0
3 A3 1 0 10,055 1 0
4 A4 0 0 0 8762 21
5 A5 0 0 2 16 8044
      Predicted Class


Performance Analysis of the Classifiers with Feature Selection of Accelerometer and Gyroscope

Table 6 shows the results of classifiers for feature selection employing accelerometer and gyroscope sensors and Figure 4.

Depicts the overall comparison of classifier results with all input postures.

Different classifier results with feature selection of accelerometer and gyroscope.

Figure 4. Different classifier results with feature selection of accelerometer and gyroscope. Image Credit: Sinha, et al., 2021.

Table 6. Classifiers result with feature selection of accelerometer and gyroscope. Source: Sinha, et al., 2021.

S. No Activities KNN
(K = 3)
(K = 5)
(K = 7)
(K = 11)
SVM Naive
1 A1: Left Movement 98.24 97.88 97.56 96.95 97.10 84.55
2 A2: Right Movement 99.78 99.76 99.73 99.69 99.88 99.37
3 A3: Front Movement 99.55 99.53 99.52 99.51 99.66 92.43
4 A4: Back Movement 98.95 99.08 99.17 99.26 98.78 85.66
5 A5: Straight Movement 95.87 95.12 94.57 93.51 98.31 95.31


Table 7 illustrates the confusion matrix for all considered sitting behaviors of office workers by employing an accelerometer and gyroscope with the SVM classifier.

Table 7. Confusion matrix of SVM classifier of selected features of accelerometer and gyroscope. Source: Sinha, et al., 2021.

  S. No Activity A1 A2 A3 A4 A5
True Class 1 A1 10,341 7 108 8 185
2 A2 3 11,460 3 2 5
3 A3 130 2 10,038 1 2
4 A4 1 1 0 8746 106
5 A5 69 4 1 59 7868
      Predicted Class


Performance Analysis of the Classifiers with Feature Selection of Accelerometer

Table 8 shows the result of all the applied classifiers by employing the accelerometer sensor and the confusion matrix in Table 9 is generated for five various sitting behaviors confused with each other. 

Table 8. Classifier results with feature selection of accelerometer. Source: Sinha, et al., 2021

S. No Activities KNN
(K = 3)
(K = 5)
(K = 7)
(K = 11)
SVM Naive
1 A1: Left movement 99.63 99.57 99.50 99.46 98.94 82.18
2 A2: Right movement 99.95 99.93 99.92 99.91 99.89 99.80
3 A3: Front movement 99.90 99.88 99.87 99.87 99.84 90.62
4 A4: Back movement 99.68 99.72 99.70 99.75 99.60 91.06
5 A5: Straight movement 99.31 99.20 99.15 98.88 99.30 96.28


Table 9. Confusion matrix of KNN (K = 3) classifier of selected features of accelerometer. Source: Sinha, et al., 2021

  S. No Activity A1 A2 A3 A4 A5
True Class 1 A1 10,634 6 18 1 14
2 A2 3 11,467 1 0 1
3 A3 9 0 10,049 0 1
4 A4 1 0 0 8769 127
5 A5 15 1 0 39 7994
      Predicted Class


Analysis of Results

The current study used smartphone technology as the sensor for analysis of the sitting behavior of test subjects. Based on the results, it was noted that the SVM classifier outdid the other classifiers. However, when considering the other classifiers, the performance is not that precise while considering all the postures.


The study analyzed five general sitting behaviors of office workers by employing the inertial sensor inbuilt in a smartphone with the help of machine learning classification techniques. Researchers have also performed a comparative analysis of different activity recognition techniques and found that 99.90% accuracy was achieved for all sitting behaviors on the office chair by the SVM classifier.

Continue reading: Wireless 3D Printed Wearable Sensor to Track Health and Body Function.

Journal Reference:

Sinha, V. K., Patro, K. K., Pławiak, P., Prakash, A. J. (2021) Smartphone-Based Human Sitting Behaviors Recognition Using Inertial Sensor. Sensors, 21(19), p. 6652. Available at: https://doi.org/10.3390/s21196652.

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Megan Craig

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Megan Craig

Megan graduated from The University of Manchester with a B.Sc. in Genetics, and decided to pursue an M.Sc. in Science and Health Communication due to her passion for learning about and sharing scientific innovations. During her time at AZoNetwork, Megan has interviewed key Thought Leaders across several scientific, medical and engineering sectors and attended prominent exhibitions worldwide.


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