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Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model

1
Department of Computer Science, Air University, Islamabad 44000, Pakistan
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Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 579; https://doi.org/10.3390/e22050579
Received: 18 April 2020 / Revised: 8 May 2020 / Accepted: 19 May 2020 / Published: 20 May 2020
Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance. View Full-Text
Keywords: Adam optimization; accelerometer and gyroscope sensors; inertial sensors; multi-fused features; maximum entropy Markov model Adam optimization; accelerometer and gyroscope sensors; inertial sensors; multi-fused features; maximum entropy Markov model
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Tahir, S.B.; Jalal, A.; Kim, K. Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model. Entropy 2020, 22, 579.

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