A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment
Abstract
:1. Introduction
2. Literature Review
2.1. Audio Recognition and Activity Detection Using Audio Signals
2.2. Activity Detection Using Kinematic Signals
2.3. Audio and Kinematic Data Comparison
3. Materials and Methods
3.1. Recording Audio and Kinematic Signals
3.2. Pre-Processing Data
- Removing gravity: This step is only applied on an accelerometer sensor because its values are subject to both dynamic (or external) and static (or gravity) accelerations. Thus, gravity components need to be eliminated from the signal. Equation (1) shows a low-pass filter for accelerometer sensor values. The cut-off frequency of 0.1 to 0.5 Hz is recommended to remove the gravity component from the data [53,54]. This equation calculates g-values for the sensor (g is initially set to zero) and then in Equation (2), g-values are subtracted from the sensor values. More details about these equations can be found in Bayat et al. [55].g(t) = (1 − a) × g(t − 1) + a × s(t) and g(0) = 0,s(t) = s(t) − g(t),
- Removing outliers: Outliers are unwanted noise or behaviors significantly different from the rest of the data. They decrease the accuracy of the system, so they should be eliminated. Data smoothing consists of techniques for removing these data points. Moving window methods are utilized to analyze data in smaller groups at a time. In this paper, the authors tested the moving window medians of length 3, 6, 12, and 24 using cross-validation. It is found that the window size of 3 is more effective to detect outliers.
- Filling missing values: During recording data, and due to connection issues, some data points might be missed or not be recorded. These missing values affect the accuracy of the system. Thus, it is crucial to find a way to fill these values. The method for filling missing values is the same as detecting and removing outliers. Similar to the chosen window size for removing outliers, the authors tested different window sizes and found that window size 24 has less impact on the data.
3.3. Selecting and Extracting Features
3.4. Dimension Reduction
3.5. Sensor Fusion
3.6. Support Vector Machines (SVM) Model
3.7. Smoothing Labels
4. Experimental Setup and Results
5. Discussion
5.1. Audio or Kinematic Data?
5.2. Role of Data Fusion
5.3. Applications of Equipment Activity
- Maintenance Assessment: Collecting data from different sensors and recognizing equipment activities can further be used as a platform for monitoring its abnormality or well-being, fuel consumption evaluation, and utilization time and cycle time estimation. Using an automated real-time framework, construction managers are able to continuously monitor activities of equipment using a standalone device or a mobile app. They can be provided with productivity rates and make proper decisions based on the performance of the equipment. For example, they can be notified if an equipment productivity rate is low and make proper decisions such as repairing the equipment, changing the equipment, or even changing the equipment operator.
- Environmental Performance Monitoring: Construction equipment usually releases detrimental smoke, which makes the construction job site unhealthy and unsafe for personnel. Also, it has harmful impacts on the environment. Thus, continuous monitoring of equipment can help construction managers track the emissions of the equipment during its operations and find its potential deficiencies. In other words, engine audio and kinematic data of equipment can be compared with the new models’ data and identify any abnormality to prevent more pollution.
6. Conclusions
- Construction job sites may vary in types of existing equipment, weather, complexity, etc. Some types of equipment, especially new models, might not generate kinematic signals and this would make it almost impossible to detect activities. Also, distance and inaccessibility of equipment or the presence of sound barriers may hinder the process of recording audios. Rainy, big and crowded job sites might affect the accuracy of each data type and decrease the precision of the detection. In this paper, both audio and kinematic signals have been fused to overcome and cover these limitations.
- Most of the aforementioned papers utilized a few features to train the machine learning model. In this paper, different types of time-domain and frequency-domain features were selected and evaluated before using in a training model. Also, a dimension reduction method has been implemented on the feature set to decrease the correlation between features and increase the class separability of features’ values. Moreover, it decreases computational time of the process which can further be used in real-time.
- Different types of pre-processing algorithms were implemented in this paper on audio and kinematic signals which refine the data before being used in subsequent steps.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Audio and Kinematic | Only Kinematic |
---|---|
25 Short Time Fourier Transform (STFT) Coefficients | Zero Crossing Rate (ZCR) |
Root Mean Square (RMS) | |
Short Time Energy (STE) | |
Spectral Flux (SF) | |
Spectral Entropy (SE) | |
Spectral Centroid (SC) | |
Spectral Roll-Off (SRO) |
Activity | Start (s) | End (s) | Duration (s) | Activity Label |
---|---|---|---|---|
Arm Raising | 0 | 13.4 | 13.4 | 2 |
Arm Lowering | 13.4 | 24.4 | 11 | 2 |
Shovel Lowering | 24.4 | 30.4 | 6 | 2 |
Shovel Raising | 30.4 | 35.4 | 5 | 2 |
Arm Lowering | 35.4 | 36.5 | 1.1 | 2 |
Moving Forward | 36.5 | 45.97 | 9.47 | 3 |
Minor Stop | 45.97 | 46.84 | 0.87 | 1 |
Moving Backward | 46.84 | 52.6 | 5.76 | 3 |
Minor Stop | 52.6 | 53.5 | 0.9 | 1 |
Turning Right | 53.5 | 60.85 | 7.35 | 4 |
Minor Stop | 60.85 | 61.65 | 0.8 | 1 |
Turning Left | 61.65 | 70.13 | 8.48 | 4 |
Arm Raising | 70.13 | 72.89 | 2.76 | 2 |
Turning Right | 72.89 | 77.16 | 4.27 | 4 |
Arm Lowering | 77.16 | 78.84 | 1.68 | 2 |
Shovel Lowering | 78.84 | 80.47 | 1.63 | 2 |
Stop | 80.47 | 87 | 6.53 | 1 |
End | 87 |
Actual Label | Accuracy % | ||||||
---|---|---|---|---|---|---|---|
Stop | Arm/shovel Movement | Moving Forward/Backward | Turning Right/Left | ||||
Predicted Label | Vibration | Stop | 41 | 58 | 21 | 2 | 87.08 |
Arm/shovel movement | 19 | 692 | 6 | 16 | |||
Moving forward/backward | 19 | 15 | 227 | 1 | |||
Turning right/left | 9 | 22 | 1 | 314 | |||
Audio | Stop | 11 | 94 | 10 | 7 | 74.71 | |
Arm/shovel movement | 3 | 690 | 19 | 21 | |||
Moving forward/backward | 5 | 34 | 164 | 59 | |||
Turning right/left | 1 | 73 | 44 | 228 | |||
Fused Data | Stop | 66 | 41 | 10 | 5 | 92.00 | |
Arm/shovel movement | 17 | 697 | 1 | 18 | |||
Moving forward/backward | 6 | 4 | 252 | 0 | |||
Turning right/left | 2 | 11 | 2 | 331 |
Vibration Accuracy | Audio Accuracy | Fused Data Accuracy | |||||||
---|---|---|---|---|---|---|---|---|---|
Low | Moderate | High | Low | Moderate | High | Low | Moderate | High | |
Jackhammer 305.5E2 | ✔ | ✔ | ✔ | ||||||
CAT 259D | ✔ | ✔ | ✔ | ||||||
Skyjack SJ6826 | ✔ | ✔ | ✔ | ||||||
XTREME 842Lift | ✔ | ✔ | ✔ | ||||||
CAT 308E | ✔ | ✔ | ✔ | ||||||
CAT 305.5E2 | ✔ | ✔ | ✔ | ||||||
Dozer 850K | ✔ | ✔ | ✔ | ||||||
CAT 938M | ✔ | ✔ | ✔ | ||||||
CAT 210G Vibrator | ✔ | ✔ | ✔ | ||||||
Concrete Truck | ✔ | ✔ | ✔ |
Testing Time (s) | ||||||||
---|---|---|---|---|---|---|---|---|
Predicting Time | Total Time | |||||||
(1) Equipment | (2) Number of Activities | (3) Capturing and Pre-processing Data | (4) Vibration Data | (5) Audio Data | (6) Fused Data | (3) + (4) Vibration Data | (3) + (5) Audio Data | (3) + (6) Fused Data |
Concrete Truck | 2 | 0.918 | 0.043 | 0.008 | 0.046 | 0.961 | 0.926 | 0.964 |
XTREME 842Lift | 3 | 0.963 | 0.030 | 0.020 | 0.015 | 0.993 | 0.983 | 0.978 |
CAT 259D | 4 | 0.664 | 0.191 | 0.035 | 0.083 | 0.855 | 0.699 | 0.747 |
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Sherafat, B.; Rashidi, A.; Lee, Y.-C.; Ahn, C.R. A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment. Sensors 2019, 19, 4286. https://doi.org/10.3390/s19194286
Sherafat B, Rashidi A, Lee Y-C, Ahn CR. A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment. Sensors. 2019; 19(19):4286. https://doi.org/10.3390/s19194286
Chicago/Turabian StyleSherafat, Behnam, Abbas Rashidi, Yong-Cheol Lee, and Changbum R. Ahn. 2019. "A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment" Sensors 19, no. 19: 4286. https://doi.org/10.3390/s19194286