Wearables Development Toolkit (WDK)

The Wearables Development Toolkit (WDK) is a framework and set of tools to facilitate the iterative development of activity recognition applications with wearable and IoT devices. It supports the annotation of time series data, the analysis and visualization of data to identify patterns and the development and performance assessment of activity recognition algorithms. At the core of the WDK is a repository of high-level components that encapsulate functionality used across activity recognition applications. These components can be used within a Matlab script or within a visual flo-based programming platform (i.e. Node-RED).

To get a first insight into the WDK, watch this demo video: https://www.youtube.com/embed/Ow0b0vkciDs and read my paper.

To install and use the WDK, refer to the Documentation.

1- Data Annotation

An annotated data set is needed to train a machine learning algorithm and to assess its performance. The Data Annotation App offers functionality to annotate time series data. Depending on the particular application, one might want to annotate events that occurr at a specific moment in time or activities that have a duration in time, called ranges. The following image shows the squared magnitude of the accelerometer signal collected by a motion sensor attached at a hind leg of a cow. The individual strides of the cow have been annotated as event annotations (red) and the walking and running activities as ranges (black rectangles).

Annotating with video (optional)

The Data Annotation App can load and display videos next to the data wich is synchronized by specifying at least two data samples and two video frames that correspond to the same event in time. The frames in the video file are displayed by the Movie Player at the bottom right of the window:

In this application, we asked the subject to applaud three times in front of the camera while wearing an armband with an Inertial Measurement Unit (IMU). We matched the samples at the peak of squared magnitude of acceleration to the video frames where the subject's hands make contact with each other.

Please note:

1. The Data Annotation App synchronises video and data at two points and interpolates linearly inbetween. I recommend the synchronization points to take place in the beginning and end of a recording.
2. Annotation, marker, synchronization and video files should be consistent with the data files. If a data file is named 'S1.mat', its annotation file should be named 'S1-annotations.txt', its synchronization file 'S1-synchronization.txt' and the video 'S1-video.'.
3. By default, the Data Annotation App loads annotation files from the './data/annotations/', video and synchronization files from './data/videos' directory. Saved annotation files are located in the root './' directory.
4. The labels to annotate should be defined in the 'labels.txt' file beforehand.
5. You can use the keyboard shortcuts arrow-right, arrow-left and spacebar to iterate through data and video.

Automatic Annotation (optional)

The Data Annotation App offers two features to facilitate the annotation of sensor signals.

Unsupervised automatic annotation

The unsupervised feature analyzes the entire data set, clusters similar portions of data and suggests annotations for some of the portions of data. This feature requires a segmentation algorithm to be provided by application developers. A set of heuristic features are extracted for each segment extracted. Finally kmeans is used to cluster the feature vectors using as many labels as defined in the current project. The N feature vectors closest in distance to a centroid are suggested to the user together with a cluster id. With a single click, the user can modify the suggested cluster into the appropriate class. The parameter N is configurable over the user interface.

Supervised automatic annotation

The supervised feature searches for portions of data similar to previously added range annotations. When a range annotation is added while the supervised auto-annotation is enabled, the Data Annotation App scans the current file for segments of data of the same length and compares the segments to the range annotation using Dynamic Time Warping. The resulting segments are sorted and the N most similar segments are suggested to the user. The parameter N is configurable over the user interface.

2- Data Analysis

The Data Analysis App displays segments of data grouped by class. This is useful to study the differences across classes to design a recognition algorithm able to discriminate between classes. Segments can be plotted either on top of each other or sequentially (i.e. after each other).

In order to visualize data:

1. Select one or more input data files.
2. Select where the segments should come from. Manual annotations creates segments from the range annotations and loads event annotations to create segments using the ManualSegmentationStrategy. The Automatic segmentation uses a preprocessing, event detection and segmentation algorithms selected over the user interface to create segments.
3. (in Automatic segmentation mode) Select the signals to use, a preprocessing algorithm and (optionally) an event detection algorithm.
4. Select a segmentation strategy and (optionally) a grouping strategy. Click the Execute button. At this point the segments are created. A grouping strategy maps annotated labels to classes, usually by grouping different labels into classes.
5. Select signals and classes to visualize and a plot style (i.e. overlapping or sequential).

3- Algorithm Implementation

Most wearable device applications execute a sequence of computations to recognize specific patterns based on sensor signals. This sequence of computations is called the Activity Recognition Chain and consists of the following stages:

Programming

Activity recognition applications can be developed directly in Matlab using the WDK's framework of reusable components.

The following text snippet creates a chain of computations and saves it to the goalkeeperChain.mat file. This chain of computations detects events using a peak detector on the squared magnitude of the accelerometer signal, segments the data around the detected events (200 samples to the left of the event and 30 sampels to the right) and extracts the features defined in the goalkeeperFeatureChain.mat file.

%select first three axes of acceleration
axisSelector = AxisSelector(1:3);%AX AY AZ

%compute the magnitude of acceleration
magnitudeSquared = MagnitudeSquared();

%detect peaks on the magnitude of acceleration
simplePeakDetector = SimplePeakDetector();
simplePeakDetector.minPeakHeight = single(0.8);
simplePeakDetector.minPeakDistance  = int32(100);

%create segments around detected peaks
eventSegmentation = EventSegmentation();
eventSegmentation.segmentSizeLeft = 200;
eventSegmentation.segmentSizeRight = 30;

%label created segments
labeler = EventSegmentsLabeler();

%load feature extraction algorithm
featureExtractor = DataLoader.LoadComputer('goalkeeperFeatureChain.mat');

%create the recognition algorithm
arcChain = Computer.ComputerWithSequence({FileLoader(),PropertyGetter('data'),...
axisSelector,magnitudeSquared,simplePeakDetector,eventSegmentation,labeler,...
featureExtractor});

%export the recognition algorithm
DataLoader.SaveComputer(arcChain,'goalkeeperChain.mat');

This chain of computations produces a feature table that can be used within the Assessment App to study the performance of different machine learning algorithms.

Visual Programming (optional)

Activity recognition applications can also be developed visually in Node-RED using the nodes available in the WDK-RED platform. The following image shows an activity recognition chain for detecting and classifying soccer goalkeeper training exercises using a wearable motion sensor attached to a glove worn by a goalkeeper:

Activity Recognition Chains can be imported and executed in the WDK as follows:

• Create and export the Activity Recognition Chains as described in the WDK-RED repository.
• Execute the convertJSONToWDK.m script.
• Use the Execute from File button in each App of the WDK.

4- Algorithm Assessment

The development and assessement / evaluation of an activity recognition algorithm usually represents a large fraction of the effort to develop the entire application. The Assessment App enables developers to design algorithms by selecting reusable components at each stage of the activity recognition chain and to assess their performance. The recognition performance metrics provided by this tool are:

• Accuracy
• Precision
• Recall
• Confusion Matrix

and the computational performance metrics are:

• Flops: number of floating point operations performed by the algorithm for the input data set
• Memory: amount of memory consumed by the algorithm (in bytes) for the input data set
• Communication: amount of bytes generated by the last component in the recognition chain

The following image shows the configuration and classification results of an algorithm to detect and classify exercises performed by patients after a hip replacement surgery.

The Performance Asessment Detail View displays the classification results on top of the data and next to the corresponding video. Green overlays indicate correctly classified segments and red overlays indicate misclassified segments.

Note: Feature tables generated with a particular activity recognition algorithm can be exported to .txt formats to study the classification on other platforms such as Python / tensorFlow and WEKA.

Reusable Components

Here you can find a list of the reusable components, their configurable properties and their performance metrics relative to an input of size n.

Preprocessing

Name	Description	Flops	Memory
HighPassFilter	Butterworth High-pass filter with order k	13 k n	1 / n
LowPassFilter	Butterworth Low-pass filter with order k	31 k n	1 / n
Magnitude		4 n	1 / n
SquaredMagnitude		2 n	1 / n
Norm		2 n	1 / n
Derivative	First derivative: . Second derivative:	40 n	1 / n
S1		40 k n	n
S2		203 k n	n

Invocations to preprocessing algorithms produce n values. The memory consumption of most preprocessing algorithms is 1 if the inPlaceComputation property is set to true or n otherwise.

Event Detection

Note: The flops metrics shown above have been calculated with random values in the range [0 1] using minPeakHeight = 0.8, minPeakDist = 100. The performance metrics will vary depending on how often peaks are detected based on the input data and minPeakHeight and minPeakDist properties.

The event detection algorithms output a single value.

An invocation to an event detection algorithm produces 0 or 1 value.

Segmentation

An invocation to a segmentation algorithm produces a segment (of segmentSize or segmentSizeLeft + segmentSizeRight values).

Labeling

Feature Extraction

Time-domain features

Name	Description	Flops	Memory
Min	Minimum value in the input signal.	n	1
Max	Maximum value in the signal.	n	1
Mean	Average of every value in the input signal.	n	1
Median	Median of the input signal.	15 n	1
Variance	Variance of the input signal.	2 n	1
STD	Standard Deviation of the input signal.	2 n	1
ZCR	Zero Crossing Rate. Amount of times the signal crosses the zero line.	5 n	1
Skewness	A measure of the asymmetry in the distribution of values in the input signal calculated as:	6 n	1
Kurtosis	Describes the "tailedness" of the distribution of values in the input signal.	6 n	1
IQR	Computes the difference between Q3 and Q1 where Q1 is the median of the n/2 smallest values and Q3 is the median of the n/2 largest values in an input signal of size n. Can be calculated in O(n) time.	57 n	n
AUC	Area under the curve computed with the trapezoid rule:	8 n	1
AAV	Average Absolute Variation:	5 n	1
Correlation	Pearson correlation coefficient of two n-dimensional inputs	3 n	n
Energy	sum of squared values in the input signal:	2 n	1
Entropy	Estimates the amount of information in the input signal. Rare events (i.e. sample values) carry more information (and have a higher entropy) than seldom events. where pi are the probability distribution values of the input signal	n^2	n
MAD	Mean Absolute Deviation. The average distance of each data point to the mean.	5 n	1
MaxCrossCorr	Maximum value of the cross correlation coefficients of two input signals. Note: input should be a nx2 array.	161 n	n
Octants	Determines the octant of each sample in an input array of n samples with 3 columns each (e.g. if all three columns are positive, octant = 1. If all 3 columns are negative, octant = 7).	7 n	1
P2P	Peak to Peak distance (distance between maximum and minimum values).	3 n	1
Quantile	Computes the numQuantileParts cutpoints that separate the distribution of samples in the input signal.	3 n log2(n)	numQuantileParts
RMS	Root Mean Squared.	2 n	1
SMV		4 n	1
SMA	Sum of absolute values of every sample (takes 2-D inputs)	n m	1

Invocations to time-domain feature extraction algorithms produce a single value except for the Quantile component which produces numQuantileParts values.

Frequency-domain features

Name	Description	Flops	Memory
FFT	FFT of the input signal	n log2(n)	n
FFTDC	DC component of the FFT of the input signal	1	1
MaxFrequency	Maximum value in the fourier transform of the input signal	n	1
PowerSpectrum	Distribution of power into frequency components. Note: outputs n coefficients	4 n	n
SpectralCentroid	Indicates where the "center of mass" of the spectrum is located.	10 n	1
SpectralEnergy	The energy of the frequency domain (sum of squared values of dft coefficients).	2 n	1
SpectralEntropy	Indicates how chaotic / how much informatiomn there is in the frequency distribution. Calculated as: where yi are the coefficients of the power spectrum of the input signal	21 n	1
SpectralFlatness	Provides a way to quantify how noise-like a sound is. White noise has peaks in all frequencies making its spectrum look flat.	68 n	1
SpectralSpread	Indicates the variance in the distribution of frequencies.	11 n	1

Invocations to frequency-domain feature extraction algorithms output a single value except for the FFT and PowerSpectrum which produce n/2 and n values respectively.

Classification

The performance metrics of each classifier depend on its configuration and are calculated at runtime.

Postprocessing

The postprocessing components produce a label as output.

Utilities

The amount of memory and output size of the PropertyGetter and NoOp modules depend on their input and configuraton values and are computed at runtime.

References

• My paper describing the Wearables Development Toolkit WDK paper and BibTeX file
• My Matlab tutorial on Activity Recognition for wearables: https://github.com/avenix/ARC-Tutorial/
• Andreas Bulling's tutorial on Activity Recognition
• Peak detection algorithms by Palshikar
• mRMR feature selection by Peng
• Nick Gillian's Gesture Recognition Toolki

Applications developed with the WDK:

1. https://www.mdpi.com/2414-4088/2/2/27
2. https://dl.acm.org/citation.cfm?id=3267267

About

My name is Juan Haladjian. I developed the Wearables Development Toolkit as part of my post-doc at the Technical University of Munich. Feel free to contact me with questions or feature requests. The project is under an MIT license. You are welcome to use the WDK, extend it and redistribute it for any purpose, as long as you give credit for it by copying the LICENSE.txt file to any copy you make.

Feel free to contact me with feedback or feature requests.

Website: www.jhaladjian.com

Academic Website: http://in.tum.de/~haladjia

LinkedIn: www.linkedin.com/in/juan-haladjian

Email: [email protected]

Cite this project

@misc{haladjian2019,
  author =       {Juan Haladjian},
  title =        {{The Wearables Development Toolkit (WDK)}},
  howpublished = {\url{https://github.com/avenix/WDK}},
  year =         {2019}
}