Use Case: Activity Recognition

Overview

Sensor-based activity recognition can be used to model a wide range of human activities. Mobile devices, such as smart phones provide sufficient sensor data and calculation power to enable physical activity recognition to provide an estimation of the energy consumption during everyday life.

Sensor-based activity recognition researchers believe that by empowering ubiquitous computers and sensors to monitor the behavior of agents (under consent), these computers will be better suited to act on our behalf (see Wikipedia).

In this use case, we would like to showcase the recognition of the human activities - sitting, jumping and running. For the purpose of showcasing the same, we followed these steps:

Prerequisites

Download the ActivityRecognitionDemo.zip file which contains demo scripts, training data set, PMML Model and EPL rule.

Running the demo scripts requires

Getting Started

We have added a CONFIG.INI file to the zip. This file is meant for capturing the tenant details and credentials which will be used by the demo scripts.

First of all, update the CONFIG.INI with the appropriate values and save it. Replace c_url with your tenant URL, c_user with your tenant username and c_pass with your tenant password. Leave the c_device_source as is for now.

CONFIG.INI
[cumulocity]
c_url=https://yourtenant.cumulocity.com
c_user=user@company.com
c_pass=password
c_device_source=deviceID

For this particular demo, a phone or a phone-like device needs to be used, so that the measurement data for that particular device can be captured and be used for recognizing activities.

Therefore, the documentation has been split up into two parts:

Activity recognition using an iPhone

This section deals with the basic data science steps of creating an activity recognition model with self-collected data. First of all, you need to register your iPhone. Then follow the sections below for collecting data, training the model and using the model to recognize activities via the phone. Note, that the phone for the entire workflow has to be of the same type because the data and sensors for device types may differ.

Register an iPhone in Cumulocity

Registering an iPhone on Cumulocity involves installing the Cloud Sensor App on your phone and using it for completing the registration.

Follow the steps described in Optional services > Cumulocity IoT Sensor App in the User guide.

After the download, do the following on your iPhone: Open Settings > Click General > Click Device Management or Profiles & Device Management > Select Software AG > Trust “Software AG“.

Once registered, try to get the device ID by looking up your device on the All Devices page of your tenant’s Device Management application. Now, update the c_device_source of the CONFIG.INI file with the device ID of your registered iPhone.

Data collection with Cumulocity

Collecting training data for activity recognition is done by starting the Cumulocity Cloud Sensor App, performing each one of the activities over a few minutes and noting the exact time period. As a next step, the instructions and scripts provided below can be used to collect training data per activity type. The resulting CSV files can be combined for the different activity classes, so that the final training data includes instances for all activity types.

Note that for demo purposes, the data is fetched via REST and directly transformed into the training data set. More complex pre-processing might require the use of an offline data store. The format of the JSON data might have changed in the meantime, or some sensors might not be available for some phone types, so check the exact format by viewing a current sample.

The following code block contains the data format of the JSON schema that was assumed for this demo.

measurement.json
{
    "_id" : ObjectId("5c928ba9b524aca326d727fe"),
    "self" : "https://zdev.cumulocity.com/measurement/measurements/7668082",
    "time" : "2019-03-18T14:46:15.000-07:00",
    "id" : "7668082",
    "source" : {
        "self" : "https://zdev.cumulocity.com/inventory/managedObjects/7668076",
        "id" : "7668076"
    },
    "type" : "c8y_Acceleration",
    "c8y_Acceleration" : {
        "accelerationY" : {
            "unit" : "G",
            "value" : -0.2860107421875
        },
        "accelerationX" : {
            "unit" : "G",
            "value" : -0.1037750244140625
        },
        "accelerationZ" : {
            "unit" : "G",
            "value" : -0.9522247314453125
        }
    },
    "activity" : "none"
}

Data collection can be done by using the below shown and attached script createData.py. This Python script connects to the Cumulocity REST measurements endpoint, pulls the data, and writes it to a CSV file.

For each activity type, the name of the activity and the timestamps need to be updated in the constants ACTIVITY, DATE_FROM and DATE_TO. Note that the format of the data might look slightly different. After the data sets for all activities are created, they can be combined for model training.

createData.py
import configparser
import csv, os
import requests
import queue
import numpy as np


def add2Data(d, writer, activity):

    acc = d['c8y_Acceleration']	
    accelerationX = acc['accelerationX']
    accX_Val=accelerationX['value']
    accelerationY = acc['accelerationY']
    accY_Val=accelerationY['value']
    accelerationZ = acc['accelerationZ']
    accZ_Val=accelerationZ['value']

	
    # cutting off the first instances of the training data as the std dev is not accurate for those			
    writer.writerow([accX_Val, accY_Val, accZ_Val, activity])


# collect config from CONFIG-INI -> change user and pass
config = configparser.ConfigParser()
config.read('CONFIG.INI')

# use the dates in which a particular acitvity was perfomed
DATE_FROM="2019-05-16T13:15:36.000-07:00"
DATE_TO="2019-05-16T13:15:59.000-07:00"

# specify the recorded activity
ACTIVITY="jump"

c_measurements_endpoint="/measurement/measurements/"

# maximum page size is 2000; only taking acceleration measurements
c_params={"source":config.get("cumulocity", "c_device_source"),"pageSize":"2000", "dateFrom":DATE_FROM, "dateTo":DATE_TO, "valueFragmentType":"c8y_Acceleration"}


# get first page of json data measurements from cumulocity
c_auth=config.get("cumulocity", "c_user"),config.get("cumulocity", "c_pass")
r=requests.get(config.get("cumulocity","c_url")+c_measurements_endpoint,params=c_params, auth=c_auth) 
print("Start collecting data from: "+r.url)
print("Status code: "+str(r.status_code))

# training data file
DIR_DATA="data/"
DATA_FILE=DIR_DATA+"generated_dataset_"+ACTIVITY+".csv"

 # collect data
json_doc=r.json()
data=[]

if not os.path.exists(DIR_DATA):
    os.makedirs(DIR_DATA)
    
with open(DATA_FILE, mode='w') as training_file:
    writer = csv.writer(training_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
    writer.writerow(["accelerationX","accelerationY","accelerationZ", "activity"])
	
    first_arr=json_doc['measurements']	
	
    print("Page 1.\tCollecting data at: " +first_arr[0]['time'])
	
    for d in first_arr:	
        add2Data(d, writer, ACTIVITY)
		 

    # change the range depending on amount of training data required
    for i in range(5):
        r=requests.get(json_doc['next'], auth=c_auth) 
        next_doc=r.json()
        measure_arr=next_doc['measurements']
	
        if not measure_arr:
            print("Last page reached.")
            break
	
        print("Page "+ str(i+2)+".\tCollecting data at: "+ measure_arr[0]['time'])

        for d in measure_arr:	
            add2Data(d, writer, ACTIVITY)
			
        json_doc=next_doc

print("Data written to " + DATA_FILE)

The training data set we collected is attached as data/training_demo_data_jump.csv in the attached ActivityRecognitionDemo.zip. The data created in the above script will need to be transformed into that same format once data for all activity types is obtained.

Train the PMML model

The attached Python script createModel.py creates a Decision Tree Model in PMML format using the previously created training data. The reason we chose this model type was that it can be easily interpreted and it performed well in our evaluations.

New fields are created by calculating the standard deviation of a sample of 5 previous records. If no training data was created with the createData.py script, sample training data for the activities sit, run and jump can be found under data/training_demo_data_jump.csv of the attached ZIP file. It is then used for training the model with the help of the scikit-learn framework (https://scikit-learn.org). To obtain a robust and meaningful model, further cleaning of the training data and validating the best model parameters is required. This is not in the scope of this demo and presumes knowledge of data science. After the model is created in scikit-learn, it is converted into PMML format with the Nyoka library (https://github.com/nyoka-pmml/nyoka).

The script also contains a 10-fold cross validation and the evaluation against some test data. This data was obtained by having an additional person perform the three activities. The resulting numbers can give an outlook of how good the model can perform when showcasing the demo. The files that we used for testing are contained in the ZIP folder under data/test_demo_data_jump.csv. When you use your own test data, follow the same format. The script will also create a graph-based representation of the decision tree as graph_decision_tree.pdf.

createModel.py
from sklearn import tree
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.pipeline import Pipeline
from nyoka import skl_to_pmml
from nyoka.preprocessing import Lag
import graphviz
import numpy as np
import os
import pandas as pd
import warnings

warnings.filterwarnings('ignore')

DIR="data/"
FILE_NAME="demo_data_jump"
CSV=".csv"
TRAINING_FILE_NAME=DIR+"training_"+FILE_NAME+CSV
TEST_FILE_NAME=DIR+"test_"+FILE_NAME+CSV
PMML_FILE_NAME="pmml/generated_"+FILE_NAME+".pmml"

features=['accelerationX', 'accelerationY', 'accelerationZ']
category='activity'

df_train = pd.read_csv(TRAINING_FILE_NAME, header = 0)

X=df_train[features]
y=df_train[category]

df_test = pd.read_csv(TEST_FILE_NAME, header = 0)
X_test=df_test[features]
y_test=df_test[category]

lag = Lag(aggregation="stddev", value=5)

# instantiate the decision model object, here we are using a decision tree,
model_type="d_tree"
model=tree.DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=3,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=9, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=True, random_state=None,
            splitter='best')
             
# train the model
transformed_X = lag.fit_transform(X)
model.fit(transformed_X,y)
print("Model trained: ", model)
 
# the following code can be used to show all classifications for the test data
# output_classes=model.predict(X_test)
# print("Computed results: ",output_classes)
 
# compute the cross validation score
scores = cross_val_score(model, transformed_X, y, cv=10)
print("Accuracy with 10-fold cross validation:\t %0.2f (+/- %0.2f)%%" % (scores.mean(), scores.std() * 2))
 
# test the model against data that was generated from another user
transformed_X_test = lag.fit_transform(X_test)
result=model.score(transformed_X_test, y_test)
print("Accuracy against test data:\t\t {:.4%}".format(result))
 
# convert model into PMML
print("Start converting the model into PMML...")

lag_obj = Lag(aggregation="stddev", value=5)

pipeline = Pipeline([
    ('lag', lag_obj),
    (model_type, model)
])
pipeline.fit(X,y)
skl_to_pmml(pipeline, features, "activity", PMML_FILE_NAME)
print("Model with name "+PMML_FILE_NAME+" converted into PMML")
 
#create graph for visualization of the model; this will only work for decision trees
dot_data = tree.export_graphviz(model, out_file=None, feature_names=features,
                     class_names=model.classes_,
                     filled=True, rounded=True, 
                     special_characters=True) 
graph = graphviz.Source(dot_data)
graph.render("graph_"+model_type)
print("Graph with decision tree is created as graph_" + model_type + ".pdf.")

Upload the model to Cumulocity

In order to upload the model to Cumulocity, follow the steps described in Predictive Analytics application > Managing models.

A pre-trained model ActivitiesDTreeJump.pmml is also attached for reference. This activity recognition model was trained with the data available in data/training_demo_data_jump.csv mentioned in the previous section.

Create and upload Apama monitor to Cumulocity

For this active recognition scenario, we need to use Apama streaming analytics. With Apama streaming analytics, you can add your own logic to your IoT solution for immediate processing of incoming data from devices or other data sources. This user-defined logic can, e.g. alert applications of new incoming data, create new operations based on the received data (such as sending an alarm when a threshold for a sensor is exceeded), or trigger operations on devices.

We create an EPL-based monitor file and upload it to Cumulocity. As mentioned earlier, the Apama EPL monitor file takes care of reading the measurements coming from the mobile device, sending it to the Zementis microservice and raising an alarm when any change in activity is reported by our machine learning model.

Instead of creating a new monitor file, the attached RecognizeActivities.mon file can be used after making minor adjustments. Open RecognizeActivities.mon in a text editor and replace the deviceId variable with the ID of your registered device, same as c_device_source in the CONFIG.INI file mentioned above. Save your changes and upload this monitor file to your tenant. See [Deploying Apama applications as single *.mon files with Apama EPL Apps] (/apama/analytics-introduction/#single-mon-file) in the Streaming analytics guide for details on uploading Apama monitor files.

using com.apama.correlator.Component;
using com.apama.cumulocity.Alarm;
using com.apama.cumulocity.Measurement;
using com.apama.cumulocity.FindAlarm;
using com.apama.cumulocity.FindAlarmResponse;
using com.apama.cumulocity.FindAlarmResponseAck;
using com.apama.cumulocity.FindManagedObjectResponse;
using com.apama.cumulocity.FindManagedObjectResponseAck;
using com.apama.cumulocity.FindManagedObject;
using com.softwareag.connectivity.httpclient.HttpOptions;
using com.softwareag.connectivity.httpclient.HttpTransport;
using com.softwareag.connectivity.httpclient.Request;
using com.softwareag.connectivity.httpclient.Response;
using com.apama.json.JSONPlugin;
 
monitor RecognizeActivities {
	
    // Replace this value with your device id.
    string deviceId := "";
         
    // Model to be used for recognizing activities
    string modelName := "DecisionTreeClassifier";
     
    //counter to exclude first five readings
    integer counter := 0; 
    
    // counter to include first five detections for the same activity
    integer similarActivityCount := 0;
	
    // threshold value for any activity to be predicted consecutively
    integer thresholdValueForDeterminingActivity := 5; 
    
    //initializing activity tracker flags to 'idle state'
    string lastActivity := "idle state";
    string referenceActivity := "idle state";
	
    CumulocityRequestInterface cumulocity;
	
    constant string ALARM_TYPE := "ActivityRecognitionAlarm";
	
    action onload() {
        cumulocity := CumulocityRequestInterface.connectToCumulocity();
        listenAndActOnMeasurements();
    }
  
    action listenAndActOnMeasurements() {
        monitor.subscribe(Measurement.CHANNEL);
        monitor.subscribe(FindAlarmResponse.CHANNEL);
        
        on all Measurement(source = deviceId) as m {        
                         
            if(m.measurements.hasKey("c8y_Acceleration")){
                log "Received Measurement from C8Y.";
                //Gather the data
                string record := convertMeasurementToRecord(m);
                log "Sending record to zementis - "+ record;
                Request zementisRequest := cumulocity.createRequest("GET", "/service/zementis/apply/"+modelName, any());
                zementisRequest.setQueryParameter("record", record);
                zementisRequest.execute(responseHandler);
                log "EPL execution completed.";	
            }
        } 
    }
     
    action convertMeasurementToRecord(Measurement m) returns string
    {
        dictionary<string, any> json := {};
        json["accelerationX"] := m.measurements.getOrDefault("c8y_Acceleration").getOrDefault("accelerationX").value;
        json["accelerationY"] := m.measurements.getOrDefault("c8y_Acceleration").getOrDefault("accelerationY").value;
        json["accelerationZ"] := m.measurements.getOrDefault("c8y_Acceleration").getOrDefault("accelerationZ").value;
        return JSONPlugin.toJSON(json);
    } 
     
    action responseHandler(Response apiResponse) {  	
        integer statusCode := apiResponse.statusCode;
        log "Zementis responded with status -" + statusCode.toString();
        
        // Ignore first 5 records and then starting checking the outputs for 200 responses.
        // First 5 records need to be cached by Zementis Server. Hence send it out but ignore the incoming response.
        if(counter >= 5 and statusCode = 200) {
            string currentActivity := apiResponse.payload.getSequence("outputs")[0].getEntry("predicted_activity").valueToString();
            log "Last activity was : " + lastActivity + ", Current activity is : " + currentActivity + ", Reference activity is : "+ referenceActivity;
	            
            if (currentActivity =  referenceActivity) {
                similarActivityCount := similarActivityCount + 1;
                log "Similarity count is : "+ similarActivityCount.toString();
            }
            else {
                referenceActivity := currentActivity;
                similarActivityCount := 1;
                log "Similarity count is : "+ similarActivityCount.toString();
            }
	        
            // Hold on till you get 5 occurences of the same activity and for 6th activity onwards keep ignoring if its the same activity.
            if (similarActivityCount = thresholdValueForDeterminingActivity and lastActivity != referenceActivity){
                string alarmMessage := "User switched activity from '" + lastActivity + "' to '" + referenceActivity + "'.";
				
                clearOldAlarmAndSendNewAlarm(alarmMessage);
				
                // Overwrite the last activity with the last activity so that it doesn't generate duplicate alarms for the same activity.
                lastActivity := referenceActivity;
            }
        }
        counter := counter + 1;
    }
    
    action createNewAlarm(string alarmMessage) {
        send Alarm("", ALARM_TYPE, deviceId, currentTime,
                   alarmMessage, "ACTIVE", "CRITICAL", 1, new dictionary<string,any>) to Alarm.CHANNEL;
        log "Alarm added as - "+alarmMessage;
    }
    
    action clearOldAlarmAndSendNewAlarm(string alarmMessage) {
            integer reqId:= integer.getUnique();
            send FindAlarm(reqId, {"source": deviceId, "status": "ACTIVE", "type": ALARM_TYPE}) to FindAlarm.CHANNEL;
            //only if old alarm is found, clear it
            on FindAlarmResponse(reqId=reqId) as alarmResponse and not FindAlarmResponseAck(reqId=reqId) {
                send Alarm(alarmResponse.id, ALARM_TYPE, deviceId, currentTime, alarmResponse.alarm.text,
                            "CLEARED", alarmResponse.alarm.severity, 1, new dictionary<string, any>) to Alarm.CHANNEL;
                log "Old Alarm cleared: " + alarmResponse.alarm.text;
            }
            reqId:= integer.getUnique();
            // now create a new alarm
            send FindAlarm(reqId, {"source": deviceId, "status": "ACTIVE", "type": ALARM_TYPE}) to FindAlarm.CHANNEL;
            on FindAlarmResponseAck(reqId=reqId){
                //Now create new alarm
                createNewAlarm(alarmMessage); 
            }
    }
    
    /** Cumulocity Request Interface.
    *
    * This is for making generic REST requests to other
    * Cumulocity microservices with JSON payloads.
    */
   event CumulocityRequestInterface
   {
      /** @private */
      HttpTransport transport;
       
      /**
      * Allows configuration of a HTTPTransport with
      * Cumulocity-specific configuration details.
      *
      * @returns The instance of the event that contains a transport
      */
      static action connectToCumulocity() returns CumulocityRequestInterface
      {
         string baseUrl := "";
         string basePath := "";
         string host := "";
         integer port := 0;
         string user := "";
         string password := "";
         boolean https := true;
         string tlsFile := "";
    
         dictionary<string, string> config := {};
         dictionary<string, string> envp := Component.getInfo("envp");
       
          
         if envp.hasKey("C8Y_BASEURL") and envp["C8Y_BASEURL"] != "" { // Running internal
            baseUrl := envp["C8Y_BASEURL"];
             
            user := envp["C8Y_TENANT"] + "/" + envp["C8Y_USER"];
            password :=envp["C8Y_PASSWORD"];
         }
         else { // Get the settings from the config properties when running remotely
            string k;
            dictionary<string, string> props := Component.getConfigProperties();
            for k in props.keys() {
               if (k = "CUMULOCITY_SERVER_URL") {
                  baseUrl := props[k];
               }
               else if (k = "CUMULOCITY_USERNAME"){
                  user := props[k];
               }
               else if (k = "CUMULOCITY_PASSWORD"){
                  password := props[k];
               }
               else if (k = "CUMULOCITY_TLS_CERT_AUTH_FILE"){
                  tlsFile := props[k];
               }
            }       
         }
    
         if baseUrl.find("/") < 0 {
            baseUrl := baseUrl + "/";
         }
      
         // Check if the baseUrl starts with either http or https
         if baseUrl.length()>=7 and baseUrl.substring(0,7).toLower() = "http://"{
            https := false;
            baseUrl := baseUrl.substring(7, baseUrl.length());
         }
         else if baseUrl.length()>=8 and baseUrl.substring(0,8).toLower() = "https://"{
            https := true;
            baseUrl := baseUrl.substring(8, baseUrl.length());
         }
         // Otherwise assume HTTPS and that the URL does not have such a prefix as http or https
    
         basePath := baseUrl.replace("[^/]*(/.*)?", "$1");
         host := baseUrl.replace("(?:(.*):|(.*)/|(.*)).*", "$1$2$3");
         port := baseUrl.replace("[^:]*:([0-9]*).*", "$1").toInteger();
         if (port = 0){
            if https = true{
               port := 443;
            }
            else{
               port := 80;
            }
         }
          
         config := {
            HttpTransport.CONFIG_USERNAME:user,
            HttpTransport.CONFIG_PASSWORD:password,
            HttpTransport.CONFIG_AUTH_TYPE:"HTTP_BASIC",
            HttpTransport.CONFIG_BASE_PATH:basePath
         };
          
         if https = true{
            config.add(HttpTransport.CONFIG_TLS,"true");
            config.add(HttpTransport.CONFIG_TLS_CERT_AUTH_FILE,tlsFile);
            config.add(HttpTransport.CONFIG_TLS_ACCEPT_UNRECOGNIZED_CERTS,"true");
         }
          
          
         log config.toString() at DEBUG;
         return CumulocityRequestInterface(HttpTransport.getOrCreateWithConfigurations(host, port, config));
      }
       
      /**
      * Allows creation of a request on a transport that
      * has been configured for a Cumulocity connection.
      *
      * @param method The type of HTTP request, for example "GET".
      * @param path A specific path to be appended to the request.
      * @param payload A dictionary of elements to be included in the request.
      */
      action createRequest(string method, string path, any payload) returns Request
      { 
         return transport.createRequest(method, path, payload, new HttpOptions);
      }
   }
}

Classify activities

Now that you have all the pieces together, you can try to recognize change in activity patterns with your phone. You could sit down, start jumping or running along with your mobile phone.

You should be able to see alarms being generated from your device which will be visible under the Alarms tab of your device in the Device Management application.

Activity recognition using a demo device

A fully functional demo can be prepared with the help of a demo device. For this, use the artefacts provided as part of the ActivityRecognitionDemo.zip file.

Register a demo device in Cumulocity

Instead of registering a real phone for active recognition use case, a demo device can be registered. This device can be used as a replica of an actual mobile phone.

We have added a script DemoDeviceCreator.sh which registers a demo device in Cumulocity. Run the script using the following command:

sh DemoDeviceCreator.sh

Use this script to add a device named “DemoDevice” to Cumulocity.

DemoDeviceCreator.sh
c_url=$(awk -F "=" '/c_url/ {print $2}' ./CONFIG.INI)
c_user=$(awk -F "=" '/c_user/ {print $2}' ./CONFIG.INI)
c_pass=$(awk -F "=" '/c_pass/ {print $2}' ./CONFIG.INI)
echo
echo "#####################################"
echo "#    Registering new demo device    #"
echo "#####################################"
curl --user $c_user:$c_pass -X POST $c_url"/inventory/managedObjects" -H "accept: application/json" -H "Content-Type: application/json" \
--data '{"name": "DemoDevice", "c8y_IsDevice": {}, "myDemoDevice":{}, "c8y_SupportedMeasurements": ["c8y_SignalStrengthWifi","c8y_Acceleration", "c8y_Barometer", "c8y_Gyroscope", "c8y_Luxometer", "c8y_Compass"]}'
echo
echo
echo "#########################################################"
echo "#  Registered a demo device with the name 'DemoDevice'  #"
echo "#########################################################"

Once registered, try to get the device ID by looking up your device on the All Devices page of your tenant’s Device Management application. Now, update the c_device_source of the CONFIG.INI file with the device ID of this demo device.

Upload the model and Apama monitor to Cumulocity

  1. Upload the attached model ActivitiesDTreeJump.pmml to Cumulocity. To upload the model to Cumulocity, follow the steps described in Predictive Analytics application > Managing models.

  2. Download the RecognizeActivities.mon file, open it in a text editor and replace the deviceId variable with the ID of your registered device, same as c_device_source in the CONFIG.INI file mentioned above.

  3. Save your changes and upload this monitor file to your tenant. See [Deploying Apama applications as single *.mon files with Apama EPL Apps] (/apama/analytics-introduction/#single-mon-file) in the Streaming analytics guide for details on uploading Apama monitor files.

Classify activities

A script ActivitySimulatorForDemoDevice.sh has been attached which simulates sending of readings corresponding to different activities to Cumulocity from our demo device. This script can be used to depict the recognition of various activities.

All you need to do is run it as sh ActivitySimulatorForDemoDevice.sh.

ActivitySimulatorForDemoDevice.sh
c_url=$(awk -F "=" '/c_url/ {print $2}' ./CONFIG.INI)
c_user=$(awk -F "=" '/c_user/ {print $2}' ./CONFIG.INI)
c_pass=$(awk -F "=" '/c_pass/ {print $2}' ./CONFIG.INI)
c_device_source=$(awk -F "=" '/c_device_source/ {print $2}' ./CONFIG.INI)
CURRENT_TIME=$(date --iso-8601=seconds)
echo
echo "##################################################"
echo "# Preparing simulations for Activity Recognition #"
echo "##################################################"
echo
curl --user $c_user:$c_pass -X POST $c_url"/measurement/measurements" -H "accept: application/vnd.com.nsn.cumulocity.measurementCollection+json" -H "Content-Type: application/json" \
--data '{"measurements":[{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.889587402},"accelerationX":{"unit":"G","value":0.066757202},"accelerationZ":{"unit":"G","value":-0.427780151}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.889602661},"accelerationX":{"unit":"G","value":0.068435669},"accelerationZ":{"unit":"G","value":-0.428604126}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.896789551},"accelerationX":{"unit":"G","value":0.06918335},"accelerationZ":{"unit":"G","value":-0.415237427}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.888275146},"accelerationX":{"unit":"G","value":0.076705933},"accelerationZ":{"unit":"G","value":-0.435089111}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.90007019},"accelerationX":{"unit":"G","value":0.076675415},"accelerationZ":{"unit":"G","value":-0.426757813}}}]}'
echo
echo "##########################################"
echo "#     Simulating user to be SITTING      #"
echo "##########################################"
echo
curl --user $c_user:$c_pass -X POST $c_url"/measurement/measurements" -H "accept: application/vnd.com.nsn.cumulocity.measurementCollection+json" -H "Content-Type: application/json" \
--data '{"measurements":[{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.925170898},"accelerationX":{"unit":"G","value":-0.047470093},"accelerationZ":{"unit":"G","value":-0.378372192}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.747741699},"accelerationX":{"unit":"G","value":-0.02456665},"accelerationZ":{"unit":"G","value":-0.595901489}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.886535645},"accelerationX":{"unit":"G","value":0.087524414},"accelerationZ":{"unit":"G","value":-0.456680298}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.786361694},"accelerationX":{"unit":"G","value":0.002182007},"accelerationZ":{"unit":"G","value":-0.664413452}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.78742981},"accelerationX":{"unit":"G","value":-0.112869263},"accelerationZ":{"unit":"G","value":-0.649856567}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.925170898},"accelerationX":{"unit":"G","value":-0.047470093},"accelerationZ":{"unit":"G","value":-0.378372192}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.747741699},"accelerationX":{"unit":"G","value":-0.02456665},"accelerationZ":{"unit":"G","value":-0.595901489}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.886535645},"accelerationX":{"unit":"G","value":0.087524414},"accelerationZ":{"unit":"G","value":-0.456680298}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.786361694},"accelerationX":{"unit":"G","value":0.002182007},"accelerationZ":{"unit":"G","value":-0.664413452}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.78742981},"accelerationX":{"unit":"G","value":-0.112869263},"accelerationZ":{"unit":"G","value":-0.649856567}}}]}'
echo
echo "##########################################"
echo "#     Simulating user to be JUMPING      #"
echo "##########################################"
echo
curl --user $c_user:$c_pass -X POST $c_url"/measurement/measurements" -H "accept: application/vnd.com.nsn.cumulocity.measurementCollection+json" -H "Content-Type: application/json" \
--data '{"measurements":[{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-2.854751587},"accelerationX":{"unit":"G","value":0.168869019},"accelerationZ":{"unit":"G","value":-2.098526001}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.172515869},"accelerationX":{"unit":"G","value":-0.126083374},"accelerationZ":{"unit":"G","value":-0.414993286}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-1.876571655},"accelerationX":{"unit":"G","value":0.395050049},"accelerationZ":{"unit":"G","value":-1.201461792}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.185501099},"accelerationX":{"unit":"G","value":0.027557373},"accelerationZ":{"unit":"G","value":-0.057159424}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.189147949},"accelerationX":{"unit":"G","value":-0.138885498},"accelerationZ":{"unit":"G","value":-0.07699585}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-2.854751587},"accelerationX":{"unit":"G","value":0.168869019},"accelerationZ":{"unit":"G","value":-2.098526001}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.172515869},"accelerationX":{"unit":"G","value":-0.126083374},"accelerationZ":{"unit":"G","value":-0.414993286}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-1.876571655},"accelerationX":{"unit":"G","value":0.395050049},"accelerationZ":{"unit":"G","value":-1.201461792}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.185501099},"accelerationX":{"unit":"G","value":0.027557373},"accelerationZ":{"unit":"G","value":-0.057159424}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.189147949},"accelerationX":{"unit":"G","value":-0.138885498},"accelerationZ":{"unit":"G","value":-0.07699585}}}]}'
echo
echo "##########################################"
echo "#     Simulating user to be RUNNING      #"
echo "##########################################"
echo
curl --user $c_user:$c_pass -X POST $c_url"/measurement/measurements" -H "accept: application/vnd.com.nsn.cumulocity.measurementCollection+json" -H "Content-Type: application/json" \
--data '{"measurements":[{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.775375366},"accelerationX":{"unit":"G","value":0.612564087},"accelerationZ":{"unit":"G","value":-1.166870117}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.624053955},"accelerationX":{"unit":"G","value":0.374237061},"accelerationZ":{"unit":"G","value":-1.081665039}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.426635742},"accelerationX":{"unit":"G","value":0.213226318},"accelerationZ":{"unit":"G","value":-0.556045532}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.121704102},"accelerationX":{"unit":"G","value":-0.24861145},"accelerationZ":{"unit":"G","value":-0.651138306}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.877685547},"accelerationX":{"unit":"G","value":-0.338851929},"accelerationZ":{"unit":"G","value":-0.83241272}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.775375366},"accelerationX":{"unit":"G","value":0.612564087},"accelerationZ":{"unit":"G","value":-1.166870117}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.624053955},"accelerationX":{"unit":"G","value":0.374237061},"accelerationZ":{"unit":"G","value":-1.081665039}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.426635742},"accelerationX":{"unit":"G","value":0.213226318},"accelerationZ":{"unit":"G","value":-0.556045532}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.121704102},"accelerationX":{"unit":"G","value":-0.24861145},"accelerationZ":{"unit":"G","value":-0.651138306}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.426635742},"accelerationX":{"unit":"G","value":0.213226318},"accelerationZ":{"unit":"G","value":-0.556045532}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.121704102},"accelerationX":{"unit":"G","value":-0.24861145},"accelerationZ":{"unit":"G","value":-0.651138306}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.426635742},"accelerationX":{"unit":"G","value":0.213226318},"accelerationZ":{"unit":"G","value":-0.556045532}}}]}'
echo
echo "##########################################"
echo "#     Simulating user to be JUMPING      #"
echo "##########################################"
echo
curl --user $c_user:$c_pass -X POST $c_url"/measurement/measurements" -H "accept: application/vnd.com.nsn.cumulocity.measurementCollection+json" -H "Content-Type: application/json" \
--data '{"measurements":[{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-2.854751587},"accelerationX":{"unit":"G","value":0.168869019},"accelerationZ":{"unit":"G","value":-2.098526001}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.172515869},"accelerationX":{"unit":"G","value":-0.126083374},"accelerationZ":{"unit":"G","value":-0.414993286}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-1.876571655},"accelerationX":{"unit":"G","value":0.395050049},"accelerationZ":{"unit":"G","value":-1.201461792}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.185501099},"accelerationX":{"unit":"G","value":0.027557373},"accelerationZ":{"unit":"G","value":-0.057159424}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.189147949},"accelerationX":{"unit":"G","value":-0.138885498},"accelerationZ":{"unit":"G","value":-0.07699585}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-2.854751587},"accelerationX":{"unit":"G","value":0.168869019},"accelerationZ":{"unit":"G","value":-2.098526001}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.172515869},"accelerationX":{"unit":"G","value":-0.126083374},"accelerationZ":{"unit":"G","value":-0.414993286}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-1.876571655},"accelerationX":{"unit":"G","value":0.395050049},"accelerationZ":{"unit":"G","value":-1.201461792}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":0.185501099},"accelerationX":{"unit":"G","value":0.027557373},"accelerationZ":{"unit":"G","value":-0.057159424}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.189147949},"accelerationX":{"unit":"G","value":-0.138885498},"accelerationZ":{"unit":"G","value":-0.07699585}}}]}'
echo
echo "##########################################"
echo "#     Simulating user to be SITTING      #"
echo "##########################################"
echo
curl --user $c_user:$c_pass -X POST $c_url"/measurement/measurements" -H "accept: application/vnd.com.nsn.cumulocity.measurementCollection+json" -H "Content-Type: application/json" \
--data '{"measurements":[{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.925170898},"accelerationX":{"unit":"G","value":-0.047470093},"accelerationZ":{"unit":"G","value":-0.378372192}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.747741699},"accelerationX":{"unit":"G","value":-0.02456665},"accelerationZ":{"unit":"G","value":-0.595901489}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.886535645},"accelerationX":{"unit":"G","value":0.087524414},"accelerationZ":{"unit":"G","value":-0.456680298}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.786361694},"accelerationX":{"unit":"G","value":0.002182007},"accelerationZ":{"unit":"G","value":-0.664413452}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.78742981},"accelerationX":{"unit":"G","value":-0.112869263},"accelerationZ":{"unit":"G","value":-0.649856567}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.925170898},"accelerationX":{"unit":"G","value":-0.047470093},"accelerationZ":{"unit":"G","value":-0.378372192}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.747741699},"accelerationX":{"unit":"G","value":-0.02456665},"accelerationZ":{"unit":"G","value":-0.595901489}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.886535645},"accelerationX":{"unit":"G","value":0.087524414},"accelerationZ":{"unit":"G","value":-0.456680298}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.786361694},"accelerationX":{"unit":"G","value":0.002182007},"accelerationZ":{"unit":"G","value":-0.664413452}}},{"time":"'$CURRENT_TIME'","source":{"id":"'$c_device_source'"},"type":"c8y_Acceleration","c8y_Acceleration":{"accelerationY":{"unit":"G","value":-0.78742981},"accelerationX":{"unit":"G","value":-0.112869263},"accelerationZ":{"unit":"G","value":-0.649856567}}}]}'
echo
echo "##########################################"
echo "#      Stopping all simulations now      #"
echo "##########################################"

This should now start sending measurements to Cumulocity on behalf of your demo device. It would try to simulate transition of activities in the order sit → jump → run → jump → sit.

You should notice your device generating activity recognition alarms for every transition of activity. These alarms generated from your device will be visible under the Alarms tab of your device in the Device Management application. Click Show Cleared Alarms in the top bar to see the history of all the alarms.