# Time-Frequency Analysis of Daily Activities for Fall Detection
## 4 Discussion  
[PERSON] et al. [30] reported results of accelerometer-based algorithm to detect falls of 83.3% sensitivity and 79.1% specificity in experimental studies. [PERSON] et al. [31] developed similar studies achieving 94% specificity, but with low sensitivity (76%). [PERSON] and colleagues [32] recently proposed an algorithm that provides a good trade-off both in terms of sensitivity (83%) and specificity (97%). Other studies using accelerometer-only thresholding algorithms in the time-domain report similar sensitivity (>85%) and specificities (88-94%) [3; 4; 5]. Accuracy of these algorithms is affected by peak accelerations of many rigorous activities overlapping with those achieved during a fall event. To achieve higher sensitivity (i.e., reduction of false positives), one must increase the threshold levels (UFT) for fall detection. However, when this occurs, specificity, the detection of false positive fall events increases. In fall detection applications, these false positives may lead to increase alerts to first responders and caregivers creating increase burden on health care systems. Clearly, peak acceleration in the time-domain is an insufficient feature to classify a fall event accurately. Other investigators have attempted to increase fall detection accuracy using additional sensors (i.e., gyroscope, pressure sensor, tilt sensors) to detect biomechanical movement and posture during falls. Our previous work utilized a combined accelerometer and gyroscope strategy to increase sensitivity and specificity by additionally measuring peak body rotation during fall events. With the addition of a gyroscope, we achieved high sensitivity (96.5%) and specificity (97.4%). However, the additional sensors proposed by us and others come with additional drawbacks including sensor costs, power consumption, storage requirements, and size. For wearable devices, many of these drawbacks may be sufficient to affect usability and practicality of these systems in real-world applications. In several recent studies, frequency features are used as input for machine learning strategies to improve fall detection accuracy. [PERSON] et al. [10] developed Deep Long Term Short Term Memory (LSTM) neural network architecture to classify falls with 88.7% accuracy. [PERSON] et al. [11] used time and frequency domain features to train a deep learning network achieving 86% overall accuracy. With three deep learning models, Convolutional Neural Network (CNN), LSTM, and combination of these two models, [PERSON] et al. [6] was able to improve fall detection accuracy to approximately 94%. Use of Recurrent Neural Network is presented in [10; 33] with accuracy of 98.57%. Despite machine learning algorithms outperforming alternative techniques, they still have several limitations that are not conducive to deployment in wearable systems. They require a large amount of computing resources, cannot respond in real time, and are difficult to deploy in wearable devices that require small form factors, high data storage and low power consumption in order to improve user acceptance and usability. Compared to prior algorithms, the uniqueness of the time-frequency approach proposed is that it utilizes only one sensor (i.e., accelerometer) and can be easily implemented in wearable sensor systems without suffering in system accuracy. Sensitivity (98.4%) and specificity (98.6%) are improved compared to even our prior multisensor (i.e., accelerometer _plus_ gyroscope) approach. Analysis of the accelerometer signal in the frequency domain provides additional key features differentiating falls from other rigorous events. Validation performed using a blinded dataset demonstrates the effectiveness of the proposed hierarchical time-frequency approach in accurately detecting falls from other daily activities. False alarm rate of the system was low (4/250 for false negative and 5/350 for false positive). In addition, this strategy can be extended to develop algorithms for classification of different daily activities.