Jump to content

User talk:AnalysisDATA

Page contents not supported in other languages.
From Wikipedia, the free encyclopedia
A wearable fall detection system was proposed in [59] that could determine fall events by employing acceleration and orientation thresholds. The acceleration thresholds were obtained at the training phase from SVM, and the postural orientation thresholds were determined from the subject’s tilt angle. The system used Madgwick’s orientation filter for reducing magnetic distortion and gyroscope drift, resulting in high estimation accuracy. The IMU was placed on the waist and could communicate over Bluetooth. The system analyzed the RMS data obtained from the accelerometer and the orientation filter and could detect fall events using a threshold based algorithm. This allows implementing the algorithm for real-time applications in a low profile microprocessor. The algorithm was reported to achieve a high degree of accuracy and sensitivity. Table 4 presents a comparison of the key features and performance characteristics among the activity monitoring systems discussed above.
Proposition Feature Extraction Classification Method Sensors Sensor Placement Com. Tech. Detection Accuracy Power Req.
Activity and gait recognition system on a smartphone Fixed set of features Support Vector Machine (SVM), Bayes network, and Random Tree Accelerometer is embedded in smartphone Different walking speed >99%.
In-home, fine-grained activity recognition multimodal wearable sensors Fixed feature set Conditional random field (CRF) Smartphones’ (Samsung Galaxy S4) onboard sensors (accelerometer, gyroscope, barometer, temperature and, humidity sensor), along with Gimbal Bluetooth beacons Waist, lower back, thigh, and wrist USB Walk and run indoors, use refrigerator, clean utensil, cook, sit and eat, use bathroom sink, move from indoor to outdoor, move from outdoor to indoor, walk upstairs, and walk downstairs, stand, lie on the bed, sit on the bed, lie on the floor, sit on the floor, lie on the sofa, sit on the sofa, and sit on the toilet 19 in-home activities with >80% accuracy
Wearable device based on a 9-DOF IMU Fixed set of features Accelerometer, gyroscope, and magnetometer Limb or trunk Bluetooth Balance hazards, balance monitoring for fall prediction High correlation Streaming ~6 h Logging > 16 h
Algorithm development Time-Frequncy domain analysis Hidden Markov Model 3-axis accelerometer, 3-axis gyroscope Chest USB Walking, running, ascending upstairs, descending downstairs and standing ~95%
A real-time, adaptive algorithm for gait-event detection Two inertial and magnetic sensors ( 1 IMU = 1 accelerometer, 1 gyroscope) External part of both shanks Gait events: Initial Contact (IC), End Contact (EC) and Mid-Swing for both right and left leg while walking at three different speed F1-scores 1(IC, EC), 0.998 (IC) and 0.944 (EC) for stroke subjects F1-scores 1(IC, EC), 0.998 (IC) and 0.944 (EC) for stroke subjects
Recognition method for similar gait action Inter-class relation Ship Support vector machine, K-nearest neighbor 3 IMUs (each IMU: 1 tri-axis accelerometer,1 tri-axis gyro) Fixed at the back, left, and right waist Walking on flat ground, up/down stairs, and up/down slope ~93% average
Stochastic approximation framework Fixed set of features K–means and Gaussian Mixture Models Accelerometer Belt-like strap around the waist 3 intensity level of walking: 93.8%; 3 intensity level of running 95.6%
Power-aware feature selection for minimum processing energy Minimum cost feature selection by using a redundancy graph K-nearest neighbor 6 IMUs (each IMU has one three-axis accelerometer and a two-axis gyroscope) Waist, right wrist, left wrist, right arm, left thigh, right ankle BSN Switching between stand and sit, sit and lie, bend to grasp, rising from bending, kneeling right, rising from kneeling, look back and return, turn clockwise, step forward and backward, jumping 30% energy savings with 96.7% accuracy
Parameter optimization strategy for phase-dependent locomotion mode recognition Fixed set of features 2 IMUs, 2 pressure insoles (each having 4 pressure sensors) IMUs on the shank and the shoe, pressure sensors insole Walking, up/down stairs, and up/down slope, passive mode 88%–98%
Electronic insole for wireless monitoring of motor activities and shoe comfort Fixed set of features Humidity and temperature sensors, accelerometer and 4 pressure sensors Insole ZigBee Foot accelerations, orientation in space, temperature and moisture data 10 h of data logging
Shoe-based activity monitoringsystem (smartshoe) Fixed set of features Support vector machine, multilayer perception (MLP) Five pressure sensors (PS) and one 3-D accelerometer PS on insole and accelerometer on heel of shoe Sit, stand, walk, ascend stairs, descend stairs and cycling 99.8% ± 0.1% with MLP
A wearable device for monitoring daily use of the wrist and fingers Fixed set of features K-means 2 tri-axial magnetometers Watch-like enclosure worn on the wrist and a small neodymium ring worn on the index finger Finger and wrist movement 92%–98% with a 19%–28% STD 20.5 mA at 3.3 V
Combined kinematic models to estimate human joint angles Unscented Kalman filter Unscented Kalman filter 3 IMUs Upper arm, forearm, and wrist Shoulder internal/external rotation; flexion/extension of shoulder, elbow, and wrist, supination/pronation of forearm, wrist twist Shoulder internal/external rotation; flexion/extension of shoulder, elbow, and wrist, supination/pronation of forearm, wrist twist Average RMS angle error ~3°
Wearable device with automatic gait and balance analyzing algorithms for Alzheimer patients (AP) Fixed set of features Fixed set of features 3 IMUs (each IMU has a 3-d accelerometer, a uni-axial gyroscope, and a biaxial gyroscope On feet for gait analysis on waist for balance analysis Gait parameters and balance 30 mA at 3.7 V
IMU based fall Detection system Madgwick orientation filter Accelerometer, gyroscope, and magnetometer Accelerometer, gyroscope, and magnetometer Waist Bluetooth Backward fall, forward fall, lateral left fall, lateral right fall, syncope Accuracy: 90.37%–100% Sensitivity: 80.74%–100% 15 mA–34 mA using 3.7 V

[1]

Start a discussion with AnalysisDATA

Start a discussion