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Data Mining for Wearable Sensors in Health Monitoring
Systems: A Review of Recent Trends and Challenges
Hadi Banaee *, Mobyen Uddin Ahmed and Amy Loutfi
Center for Applied Autonomous Sensor Systems, O¨ rebro University, SE-
70182 O¨ rebro, Sweden; E-Mails: firstname.lastname@example.org (M.U.A.);
Shift in focus from data collection and simple apps (calculating steps, sleep
etc) to Data analytics based on context awareness and Personalization
Specifically we concentrated the review on the following vital sign
electrocardiogram (ECG), oxygen saturation (SpO2 ), heart rate (HR),
Photoplethysmography (PPG), blood glucose (BG), respiratory rate (RR),
and blood pressure (BP).
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Three types of data mining tasks:
Anomaly detection(including raising alarms)
Three analysis dimensions.
a) Setting in which the monitoring occurs(ex independent living)
b) Type of subjects used (ex healthy, specific illness etc)
c) How and where data is processed
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Anomaly detection techniques are often developed based on a
classification methods to distinguish the data set into normal class and
outliers. For example, support vector machines , Markov models and
Wavelet analysis are used in health monitoring systems for anomaly
a) Usually deal with short term and multivariate data sets in order to
characterize the entire the data to find discords.
b) Finding irregular patterns in vital signs time series such as abnormal
episodes in ECG pulses , SpO2 signal and blood glucose level which
mostly discover unusual temporal patterns in continuous data.
c) Use domain knowledge and predefined information to detect anomalies
for decision making such as anomaly detection in sleep episodes and
finding hazardous stress levels
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Supervised learning models where it includes feature extraction,
training and testing steps while performing the prediction of the behavior.
Examples: blood glucose level prediction, mortality prediction by
clustering electronic health data, and a predictive decision making system
for dialysis patients.
Like anomaly detection but not necessary detection abnormalities.
Examples: estimating the severity of health episodes of patients
suffering chronic disease, sleep issues such as polysomnography and
apnea, estimation and classification of health conditions and emotion
recognition. Most of these researches have used online databases with
annotated episodes in order to have sufficient and trustable real-world
disease labels to evaluate the decision making process. Considering the
complexity of the data Neural Networks and decision trees used.
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Other Data Mining Tasks for Wearable Sensors
(1) data acquisition using the adequate sensor set;
(2)transmission of data from subject to clinician;
(3)integration of data with other descriptive data; and
Several data mining techniques are applied such as wavelet analysis for
artifact reduction and data compression , rule-based methods for data
summarizing and transmitting , and Gaussian process for secure
(1) filter unusual data to remove artifacts and
(2) remove high frequency noise
Ex ECG data to remove frequency noise, the other methods in frequency
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Time Domain Spectral Domain Other Features
Mean R-R, Std R-R, Mean HR, Spectral energy [27,62], Power
Std HR , Number of R-R spectral density , Low-pass
ECG interval , Mean R-R, Std filter , Low/high -
R-R interval . frequency [39,64].
Mean, zero crossing counts,
Drift from normality range ,
SpO2 entropy , Mean, Slope , Energy, Low frequency .
Self-similarity . Entropy .
Energy, Low/high frequency , Low/high frequency ,
Mean, Slope , Mean, Wavelet coefficients of data Drift from normality range ,
HR Entropy, Co-occurrence
Self-similarity, Std . segments , Low/high
frequency, Power spectral coefficients . density .
Rise Times, Max, Min,
PPG Mean . Low/high frequency . -
BP Mean, Slope . - Rule based features .
RR Mean, Min, Max . - Residual and tidal volume .
Zero crossings count, Peak
value, Rise time (EMG) , Spectral energy (EEG) ,
Mean, Duration (GSR) , Median and mean Frequency, Bandwidth, Peaks count
Other Pick value, Min, Max Spectral energy (EMG) , (GSR) .
(SCR) , Total magnitude, Energy (GSR) .
Duration (GSR) .
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Three most popular approaches for dimension reduction in medical
domain are PCA, ICA, and LDA
Other tools for feature selection used in the literature includes threshold-
based rules, analysis of variance (ANOVA) , and Fourier transforms.
Common health parameters considered by SVM methods are ECG, HR,
and SpO2 which are mostly used in the short term and annotated form.
In general, SVM techniques are often proposed for anomaly detection
and decision making tasks in healthcare services.
The ability of the NN is to model highly nonlinear systems such as
physiological records where the correlation of the input parameters is not
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• Time Horizon (long term/short term):
Some data analysis systems in healthcare were designed to process
short signals such as few minutes of ECG data , a few hours of heart
rate or oxygen saturation and the measurement of blood pressures for
a day and even more (Blood glucose).
• Scale (large/small): considering a big number of subjects (patient or
healthy) are counted as large scale studies .
• Labeling (annotated/unlabeled):annotations also acquired using
another source of knowledge like electronic health record (EHR),
coronary syndromes, and also history of vital signs
• Single Sensor/Multi Sensors:
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• Need for Large scale monitoring in non-clinical context
• Dealing with annotated data sets: few benchmark data sets are
available also the challenge of how data annotation (labeling) can be
best done for such target groups.
• Multiple measurements: Another challenge in this field is to exploit
the multiple measurements of vital signs simultaneously. Esp with
sensor fusion techniques
• Contextual information:
• Reliability, level of trust to the system: the amount of trust between the
data analysis system and the experts who use the system for decision
• Discovering of unseen features
• Post processing