scholarly journals Physical Context Detection using Wearable Wireless Sensor Networks

2008 ◽  
Vol 4 (3) ◽  
pp. 191 ◽  
Author(s):  
Muhannad Quwaider ◽  
Subir Biswas
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Real Life ◽  
Sensor Data ◽  
Physical Context ◽  
Significant Extension ◽  
The Individual ◽  
Stochastic Identification

This paper presents the architecture of a wearable sensor network and a Hidden Markov Model (HMM) processingframework for stochastic identification of body postures andphysical contexts. The key idea is to collect multi-modal sensor data from strategically placed wireless sensors over a human subject’s body segments, and to process that using HMM in order to identify the subject’s instantaneous physical context. The key contribution of the proposed multi-modal approach is a significant extension of traditional uni-modal accelerometry in which only the individual body segment movements, without their relative proximities and orientation modalities, is used for physical context identification. Through real-life experiments with body mounted sensors it is demonstrated that while the unimodal accelerometry can be used for differentiating activityintensive postures such as walking and running, they are not effective for identification and differentiation between lowactivity postures such as sitting, standing, lying down, etc. In the proposed system, three sensor modalities namely acceleration, relative proximity and orientation are used for context identification through Hidden Markov Model (HMM) based stochastic processing. Controlled experiments using human subjects are carried out for evaluating the accuracy of the HMMidentified postures compared to a naïve threshold based mechanism over different human subjects.

scholarly journals A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

2021 ◽  
Vol 17 (8) ◽  
pp. e1009280
Author(s):  
Indie C. Garwood ◽  
Sourish Chakravarty ◽  
Jacob Donoghue ◽  
Meredith Mahnke ◽  
Pegah Kahali ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Local Field ◽  
Hidden Markov ◽  
Human Subjects ◽  
Local Field Potentials ◽  
Field Potentials ◽  
State Process ◽  
The Mean ◽  
Eeg Recordings

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs

2017 ◽  
Vol 4 (2) ◽  
pp. 94-98
Author(s):  
ShiJie Zhao ◽  
Toshihiko Sasama ◽  
Takao Kawamura ◽  
Kazunori Sugahara
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Sensor Network ◽  
Hidden Markov ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Network System ◽  
Daily Lives

We propose a human behavior detect method based on our development system of multifunctional outlet. This is a low-power sensor network system that can recognize human behavior without any wearable devices. In order to detect human regular daily behaviors, we setup various sensors in rooms and use them to record daily lives. In this paper we present a monitoring method of unusual behaviors, and it also can be used for healthcare and so on. We use Hidden Markov Model(HMM), and set two series HMM input to recognize irregular movement from daily lives, One is time sequential sensor data blocks whose sensor values are binarized and splitted by its response. And the other is time sequential labels using Support Vector Machine (SVM). In experiments, our developed sensor network system logged 34days data. HMM learns data of the first 34days that include only usual daily behaviors as training data, and then evaluates the last 8 days that include unusual behaviors. Index Terms—multifunctional outlet system; behavior detection; hidden markov model; sensor network; support vector machine. REFERENCES [1] T.Sasama, S.Iwasaki, and T.Okamoto, “Sensor Data Classification for Indoor Situation Using the Multifunctional Outlet”, The Institute of Electrinical Engineers of Japan, vol.134(7),2014,pp.949-995 [2] M.Anjali Manikannan, R.Jayarajan, “Wireless Sensor Netwrork For Lonely Elderly Perple Wellness”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, vol. 3, 2015, pp.41-45 [3] Nagender Kumar Suryadevara, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE SENSORS JOURNAL, VOL. 12, NO. 6, JUNE 2012, pp. 1965-1972. [4] iTec Co., safety confirmation system: Mimamorou, http://www.minamoro.biz/. [6] Alexander Schliep's group for bioinformatics, The General Hidden Markov Model library(GHMM), http://ghmm.sourceforge.net/. [7] Jr Joe H.Ward, Joumal of the American Statistical Association, vol58(301), 1963, pp236-244 [5] SOLXYZ Co., status monitoring system:Ima-Irumo, http://www.imairumo.com/.

scholarly journals A HIDDEN MARKOV MODEL FOR THE DETECTION OF PURE AND MIXED STRATEGY PLAY IN GAMES

2014 ◽  
Vol 31 (4) ◽  
pp. 729-752 ◽  
Author(s):  
Jason Shachat ◽  
J. Todd Swarthout ◽  
Lijia Wei
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Repeated Games ◽  
Mixed Strategy ◽  
Hidden Markov ◽  
Human Subjects ◽  
Mixed Strategies ◽  
Mixed Strategy Nash Equilibrium ◽  
Equilibrium Profile ◽  
Nonstationary Dynamics

We propose a statistical model to assess whether individuals strategically use mixed strategies in repeated games. We formulate a hidden Markov model in which the latent state space contains both pure and mixed strategies. We apply the model to data from an experiment in which human subjects repeatedly play a normal form game against a computer that always follows its part of the unique mixed strategy Nash equilibrium profile. Estimated results show significant mixed strategy play and nonstationary dynamics. We also explore the ability of the model to forecast action choice.

scholarly journals Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Da Liu ◽  
Yanan Wei ◽  
Shuxia Yang ◽  
Zhitao Guan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Electricity Market ◽  
Hidden Markov ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecast ◽  
Adaptive Weights ◽  
Generalized Regression Neural Networks ◽  
The Individual

A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM) is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM), USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM), generalized regression neural networks (GRNN), day-ahead modeling, and self-organized map (SOM) similar days modeling.

scholarly journals A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque LFP and human EEG

2020 ◽  
Author(s):  
Indie C. Garwood ◽  
Sourish Chakravarty ◽  
Jacob Donoghue ◽  
Pegah Kahali ◽  
Shubham Chamadia ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Slow Oscillation ◽  
State Transitions ◽  
State Process ◽  
The Mean ◽  
Eeg Recordings ◽  
Gamma Burst

AbstractKetamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

Hidden Markov Model based driving event detection and driver profiling from mobile inertial sensor data

2015 ◽  
Author(s):  
Saurabh Daptardar ◽  
Vignesh Lakshminarayanan ◽  
Sharath Reddy ◽  
Suraj Nair ◽  
Saswata Sahoo ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Hidden Markov ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Model Based

scholarly journals Hand Gesture Recognition Using Hidden Markov Model Algorithm

MATICS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Roro Inda Melani
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Dominant Frequency ◽  
Sensor Data ◽  
Hand Gesture ◽  
Human Hand ◽  
Hand Position ◽  
The Difference

<p><em>A</em><em>bstract</em>— Recognizing human hand gesture through the use of INS (Inertial navigation System) sensor, Hidden Markov Model (HMM) was used as a tool to recognize pattern statistically. Employing INS sensor to admit data input , it is assumed that hand gesture could be detected by analizing the acceleration and fluctuation from data sensor and the difference of hand-position in 3-axis. The INS sensor that was being used was came with 6 channels to generate signals of a 3-axis gyroscope and a 3-axis accelerometer. The acceleration fluctuated in three perpendicular directions due to different hand gestures was detected by the accelerometer, while the change of hand-position in 3-axis was detected by gyroscope. Data from sensor was exported to computer via USB (Universal Serial Bus) port.</p><p class="Abstract">During the stage of data collection, a cut algorithm was developed to pick the most significant part of the sensor data. After finishing data comparison stage, DCT (Discrete Cosine Transform) was selected to transform the signal from time domain to frequency domain. Sequences of calculation were performed to analyze the best sampling frequency to select dominant frequency of every gesture to be picked as parameter value. The parameter value used in HMM as the approach to recognize and differs gestures.</p><p> </p><em>Index Terms</em>—<em>3-axis gyroscope,</em><em> accelerometer, gesture, gesture recognition, hand gesture, human gesture</em>.

scholarly journals Dynamic Analysis on Simultaneous iEEG-MEG Data via Hidden Markov Model

2020 ◽  
Author(s):  
Siqi Zhang ◽  
Chunyan Cao ◽  
Andrew Quinn ◽  
Umesh Vivekananda ◽  
Shikun Zhan ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Brain Activity ◽  
Group Analysis ◽  
Network Activity ◽  
Sensor Data ◽  
Normal Brain ◽  
Alpha Power ◽  
Left And Right

Background: Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. While these recordings afford detailed information about local brain activity, putting this activity in context and comparing results across patients is challenging. Non-invasive whole-brain Magnetoencephalography (MEG) could help translate iEEG in the context of overall brain activity, and thereby aid group analysis and interpretation. Methods: Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy. Pre-processed MEG sensor data was projected to source space. The time delay embedded hidden Markov model (HMM) technique was applied to find recurrent sub-second patterns of network activity in a completely data-driven way. To relate MEG and iEEG results, correlations were computed between HMM state time courses and iEEG power envelopes in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Results: Five HMM states were inferred from MEG. Two of them corresponded to the left and right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. The majority of iEEG contacts were also located in left and right temporal areas and the theta/alpha power of the local field potentials (LFP) recorded from these contacts correlated with the time course of the HMM state corresponding to the temporal lobe of the respective hemisphere. Discussion: Our findings are consistent with the fact that most subjects were diagnosed with temporal epilepsy and implanted with temporal electrodes. As the placement of electrodes between patients was inconsistent, their modulation by HMM states could help group the contacts into functional clusters. This is the first time that HMM was applied to simultaneously recorded iEEG-MEG and our pipeline could be used in future similar studies.

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