Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning
- A comprehensive study on the performance of various fall detection machine learning techniques to detect a fall.
- To analyse the performance of online and offline fall detection techniques. Specifically, Fuzzy-as-a-service utilises an online real-time approach and machine learning as an offline approach.
- To evaluate the efficiency of using fuzzy versus non-fuzzy approaches in wearable sensors-based real-time applications.
2. Review of Related Literature
- Wearable devices: Herein, a subject is required to wear a device or a sensor embedded in his/her garment to track his/her posture and motion. Sensor data that is collected is used as motion signals to analyse different body movements [12,13]. A specific threshold-based sensor is used that triggers an alarm whenever the output of the sensor reaches a specific threshold. Recently, motion-based sensors have been integrated with commercially based smartphones . The advantage of using such sensors is that they provide flexibility and portability; however, their false alarm rate is high.
- Ambient Sensors: Using various sensors to record human-related data whenever a subject is in close proximity. The main idea is to identify changes in posture from standing to lying down whenever a fall occurs. The limitation of this type of sensor is that accuracy detection is limited, it is cost inefficient and false alarm occurrence is high.
- Vision-based sensors: An indoor camera is used to monitor a single subject’s movement. A variety of video-processing algorithms use fall characteristics to determine a fall occurrence such as Verso Vision. The limitation of this type of sensor is that they are immovable, and a subject is confined to a region covered by a camera.
3. Proposed System
3.1. Fuzzy Logic System
- Fuzzify each input values as a function of fuzzy membership: Implement every necessary law to quantify the fuzzy output functions. To get “crisp” performance values, de-fuzzify the blurry output functions.
- Fuzzy input set: SVM is the first input that contains three values, i.e., low, medium and high.
- Compounding: Set a minimum angle, leading to a 45° fall, and see it as a medium angle. The lower and extreme angles are 20° and 90° respectively. The size of all 0° to 180° memberships were calculated by the minimum and maximum angles that the sensor may calculate. In that basis, if the angle is >45°, then the accident is more likely to be called a collision.
- Rule base: To perform this experiment, a total of nine rules were created for identifying whether it is a fall or not.
- De-fuzzification: It is one of the main phases in the method that uses fuzzy logic to transform a fuzzy output set into a crisp value. As the input given to the system includes three values, i.e., low, medium and high, the output of the system offered three values, i.e., low, medium and high .
3.2. Methodology Using Fuzzy Logic Web Services
3.3. Modelling Approach Using Machine Learning Techniques
- k-NN: Each object is graded by a majority vote of its neighbors, and the entity being allocated to the most common class of its nearest k neighbors. The purpose of considering k-NN model is because the nearby linear accelerometer or gyroscope sensor data points may form a specific pattern, which can be used to identify a fall or non-fall.
- Decision tree classifier: In decision tree classification, branches represent independent variables and leaves represent class variables. The purpose of using decision tree classifier was to validate the effect of decision tree learning for prediction of a fall and non-fall.
- Random forest classifier: It is a classification method for learning an ensemble. It is a set of decision trees from a randomly chosen training subset. It predicts the final class by aggregating the votes from various trees for decisions.
- Extreme gradient boosting: is also an ensemble learning method and a decision tree-based algorithm where gradient descent optimization is used for minimizing errors to optimize parallel processing, tree pruning and the model’s over-fitting.
- Input Layer: It has the same dimension as of input data. In our case, we have in total 6 features where 3 features represent linear accelerometer and the remaining 3 features represent the gyroscope data.
- Hidden Layer: There are 2 hidden layers, each having 600 neurons. Rectified Linear Unit (ReLU) is used as a non-linear activation function.
- Output Layer: As this is a multi-class classification problem and here we are aiming to predict 5 classes (backside fall, forward fall, side fall, normal walk, running), there will be 5 neurons in the output layer. Each neuron represent one class and at the end whichever neuron gets the highest probability, will be the final prediction.
4. Results and Discussion
5. Conclusions and Future Scope
Conflicts of Interest
|ADL||Activity Of Daily Living|
|ANN||Artificial Neural Network|
|FLS||Fuzzy Logic System|
|FML||Fuzzy Markup Language|
|PERS||Personal Emergency Response System|
|ReLU||Rectified Linear Unit|
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|Wearable-based||Attached to the body||||2011||Multiple tri-axial accelerometers||–||–||–|
|||2015||Accelerometer and gyroscope||–||96.3%||96.2%|
|||2013||Accelerometer and gyroscope||99.38%||100%||99.38%|
|Mobile phone-based||||2011||Multiple cameras||–||99.7%||99.7%|
|Classification algorithm-based||Machine learning||||2013||Tri-axial accelerometer||100%||100%||100%|
|||2015||Accelerometer and magnetometer||97.7%||99.3%||96%|
|||2017||Accelerometer and gyroscope||99.23%||99%||99.37%|
|Threshold||||2014||Accelerometer and gyroscope||93.3%||–||–|
|||2015||Tri-axial accelerometer and gyroscope||–||–||100%|
|||2015||Thri-axis accelerometer and gyroscope||90%||96.3%||96.2%|
|||2013||Accelerometer and gyroscope||99.38%||100%||99.38%|
|TP||16||TP + FN||18|
|TN||11||TN + FP||12|
|Algorithm||Average Accuracy||Standard Deviation|
|Decision Tree classifier||96.18%||0.14%|
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Pandya, B.; Pourabdollah, A.; Lotfi, A. Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning. Technologies 2020, 8, 74. https://doi.org/10.3390/technologies8040074
Pandya B, Pourabdollah A, Lotfi A. Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning. Technologies. 2020; 8(4):74. https://doi.org/10.3390/technologies8040074Chicago/Turabian Style
Pandya, Bhavesh, Amir Pourabdollah, and Ahmad Lotfi. 2020. "Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning" Technologies 8, no. 4: 74. https://doi.org/10.3390/technologies8040074