Hidden Markov Model-Based Fall Detection With Motion Sensor Orientation Calibration: A Case for Real-Life Home Monitoring

Abstract

Falls are a major threat for senior citizens' independent living. Motion sensor technologies and automatic fall detection systems have emerged as a reliable low-cost solution to this challenge. We develop a hidden Markov model (HMM) based fall detection system to detect falls automatically using a single motion sensor for real-life home monitoring scenarios. We propose a new representation for acceleration signals in HMMs to avoid feature engineering and developed a sensor orientation calibration algorithm to resolve sensor misplacement issues (misplaced sensor location and misaligned sensor orientation) in real-world scenarios. HMM classifiers are trained to detect falls based on acceleration signal data collected from motion sensors. We collect a dataset from experiments of simulated falls and normal activities and acquired a dataset from a real-world fall repository (FARSEEING) to evaluate our system. Our system achieves positive predictive value of 0.981 and sensitivity of 0.992 on the experiment dataset with 200 fall events and 385 normal activities, and positive predictive value of 0.786 and sensitivity of 1.000 on the real-world fall dataset with 22 fall events and 2618 normal activities. Our system's results significantly outperform benchmark systems, which shows the advantage of our HMM-based fall detection system with sensor orientation calibration. Our fall detection system is able to precisely detect falls in real-life home scenarios with a reasonably low false alarm ratet.

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

“…The level of detail provided about participants varied considerably. All but three [ 31 , 38 , 40 ] of the articles stated whether participants were community dwelling, in long-term care or hospital patients. Five articles did not provide any additional descriptive information on the participants [ 23 , 24 , 35 , 37 , 40 ].…”

“…All but three [ 31 , 38 , 40 ] of the articles stated whether participants were community dwelling, in long-term care or hospital patients. Five articles did not provide any additional descriptive information on the participants [ 23 , 24 , 35 , 37 , 40 ]. The other eighteen articles describe participant’s age, twelve also provide gender information and six provide details of height and weight or BMI [ 17 , 25 , 29 , 31 , 32 , 34 ].…”

“…Fifteen studies used wearable technology and ten used non-wearable, Table 2 shows full details of the devices used in each study. Accelerometers are the most common choice of sensor and have been used in 15 of the studies [ 17 , 21 , 22 , 23 , 24 , 25 , 29 , 30 , 31 , 32 , 33 , 34 , 36 , 38 , 40 ]. Eight studies tested some form of optical sensor [ 20 , 22 , 26 , 27 , 28 , 30 , 37 , 39 ], making them the most common choice of non-wearable devices.…”

A Survey on Recent Advances in Wearable Fall Detection Systems

“…It was observed that fine kNN produced an accuracy of 99.4%. Yu et al [37] attempt to reduce errors caused by incorrect sensor positions and details an HMMbased fall detection system for the same. Sensor orientation calibrations are applied on HMM classifiers to resolve issues arising out of misplaced sensor (3-axis accelerometer) locations and misaligned sensor orientations.…”

Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model

“…A HMM model which predicts septic shock for ICU patients [10]. Rehabilitation of a deaf person is done by a DNN-HMM hybrid system for lip-reading and audio visual speech recognition (AVSR) [11], fall detection and real-life home monitoring for senior citizens [12], emotion classification by a combined SVM-HMM classifier to recognize human emotion states based on EEG signals [13]. Already a stacked HMM model has found its applications in robotics for motion intention recognition based on motion trajectories [14].…”