Fall Detection Using Bag-Type Wearable Sensors: Detailed Explanation

Fall detection is a crucial feature in wearable devices, especially for bag-type wearables. This system uses accelerometers and gyroscopes to detect sudden impacts or changes in movement, analyzing the data to determine whether a fall has occurred. Below is a detailed breakdown of how sensors can be used for fall detection and how the corresponding algorithm can be designed.

1. Sensors and Data

1) Accelerometer

• • The accelerometer measures acceleration along three axes (X, Y, and Z), which allows the system to monitor the change in motion of the user. When a fall occurs, a sudden, sharp change in acceleration is detected.
  • Example: During a fall, the acceleration can spike to levels above 15 m/s² as the person hits the ground.

2) Gyroscope

• • The gyroscope measures angular velocity, tracking the user’s rotation and tilt. In the case of a fall, there’s often a rapid tilting or rotation of the body.
  • Example: If a user falls backward, the gyroscope will detect a sudden increase in rotational speed.

3) Other Sensors (Optional)

• • Magnetometer: Can track the direction of the bag to help improve context awareness.
  • Pressure Sensors: Can help determine if the user has fallen to the ground and assess the fall’s severity by monitoring pressure changes.

2. Fall Detection Algorithm Design

The fall detection system works by processing sensor data in real time and comparing it to normal movement patterns. The algorithm consists of feature extraction, classification models, and alert systems.

1) Feature Extraction

To accurately detect falls, it’s essential to extract distinctive features from the sensor data. Falls typically exhibit sudden, sharp changes in both acceleration and angular velocity.

• Acceleration Changes:

  • A fall is marked by rapid, high-intensity acceleration (usually higher than normal movement levels). For example, the acceleration spikes when a person falls and hits the ground, as opposed to regular walking or sitting.
  • Acceleration difference: Calculate the difference between successive acceleration measurements, detecting abrupt spikes.

• Gyroscope Rotation Changes:

  • Falls involve significant tilting or rotation of the body, so the algorithm looks for sharp increases in angular velocity.
  • Rotation speed: Measure the change in rotational velocity (radians per second) to detect abnormal tilting or falling motion.

• Pattern of Acceleration and Rotation:

  • Falls show a characteristic pattern of rapid acceleration followed by a sudden decrease in speed, or sudden rotation followed by a rapid slowdown.

2) Fall Detection Algorithm

• Threshold-Based Detection (Basic Method):

  • A simple method sets specific thresholds for acceleration and gyroscope data, triggering a fall detection event if the thresholds are surpassed.
  • Example:
    • If the acceleration exceeds 15 m/s², the system registers a fall.
    • If the gyroscope’s angular velocity exceeds 2 rad/s, the system triggers a fall detection alert.

• Machine Learning-Based Approach:

  • Supervised Learning:
    • Data Collection: Record labeled data for various activities (e.g., walking, sitting, falling).
    • Algorithms:
      • Random Forest: Can classify normal vs. abnormal postures based on multi-sensor data.
      • Support Vector Machine (SVM): Uses sensor data to classify movements into “fall” or “no fall.”
    • Feature Vector: Acceleration, rotational velocity, and their temporal patterns serve as the feature vector for classification.
  • Unsupervised Learning:
    • If labeled data is scarce, Clustering (e.g., K-Means) can group data into normal and abnormal activities, detecting fall patterns.
  • Time-Series Analysis:
    • Since falls usually occur over a brief period, time-series analysis techniques like Hidden Markov Models (HMM) or Long Short-Term Memory (LSTM) networks can be used to track the temporal sequence of sensor data, improving fall detection accuracy.

3) Post-Processing and Results Analysis

• Post-Processing:

  • After detecting an initial fall event, false positives (incorrect fall detection) can be reduced by integrating data from previous and subsequent sensor readings to ensure the event was truly a fall.
  • Example: A sudden acceleration followed by a brief recovery period may not necessarily indicate a fall.

• Alert System:

  • Once a fall is detected, the system triggers alerts:
    • Vibration Alerts: The wearable vibrates to notify the user of a detected fall.
    • Smartphone Notifications: The wearable can send an alert to a smartphone app that shows the user’s location and fall status.
    • Emergency Notifications: In critical cases, the system can send an alert to emergency contacts or medical services.

3. Example Fall Detection Algorithm (python)

import numpy as np

# Set acceleration and gyroscope thresholds
acceleration_threshold = 15 # m/s^2
gyro_threshold = 2 # rad/s

# Example sensor data (acceleration and gyroscope readings)
acceleration_data = np.array([0, 0, 12, 18, 16, 19, 0]) # Acceleration data
gyro_data = np.array([0.1, 0.1, 0.5, 1.5, 2.2, 3.0, 0.2]) # Gyroscope data

# Fall detection based on acceleration threshold
if np.max(np.abs(acceleration_data)) > acceleration_threshold:
print(“Fall detected based on acceleration!”)

# Fall detection based on gyroscope threshold
if np.max(np.abs(gyro_data)) > gyro_threshold:
print(“Fall detected based on rotation!”)

4. Conclusion

The fall detection system in bag-type wearables utilizes accelerometers and gyroscopes to monitor and identify abrupt changes in motion that indicate a fall. The algorithm compares real-time sensor data against predefined thresholds, and in more complex scenarios, machine learning models can be employed to classify movements and detect falls more accurately. With effective post-processing and alert systems, such a system can provide timely fall detection and support user safety.