# SensorTrigger
# Introduction
The SensorTrigger evaluates sensor data captured by one of the following devices: the Activity Sensor Move 3 or Move 4, the Light and Activity Sensor LightMove 3 or LightMove 4, the ECG and Activity Sensor EcgMove 3 or EcgMove 4, or the EDA and Activity sensor EdaMove 3 or EdaMove 4 from movisens.
Each sensor calculates its individual parameters as it acquires and records the raw data. The sensor transmits these parameters via Bluetooth Smart in 1 value per minute periods from the selected sensor to your android smartphone.
The trigger algorithm evaluates the data, and as a result of the evaluation, a trigger may activate a questionnaire on the Experience Sampling Platform movisensXS.
You can select from different predefined algorithms to process the sensor data. These algorithms can be configured individually for each study, or even each participant within a study. The individual algorithms are described in the next chapter. The following figure shows the block diagram of the whole system:
# Getting started
If you want to use the movisens Sensor trigger feature (beta) please drop us a line under support@movisens.com and tell us the Email address of your movisensXS account and which trigger algorithm you want to use
Update your sensor to at least firmware version 1.12.7 using the latest SensorManager (opens new window)
Configure your movisensXS study to use the latest library
Install the Beta Version of the movisensXS App as described here.
- If you have the old movisensXS Trigger App installed please uninstall it
WARNING
This feature is in beta. Please tell us if you find issues. Please use the "Send debug logs" feature in the movisensXS app to send us logging information.
# Configure your sampling schema
When all installation steps are succeeded, you see a new category in the sampling scheme called Sensors.
Depending on the granted features, you will find the following blocks in the Sensors category:
- The Generic Sensor Value condition
- General Sensor Trigger featues
- Log Sensor Connection State: Logs connection states of coupled movisens sensors.
- Sensor Battery Low: Triggers when one of the coupled sensors has a low battery.
- Sensor Disconnected: As soon as the Sensor is disconnected from the Smartphone.
- Sensor Stopped: As soon as the Sensor stopped and cannot be restarted (e.g. Battery was empty).
- Stop Sensor: This is optional, but it enables you to stop the measurement of the sensor e.g. when the study ends.
- Optional: Specialized algorithm blocks
# Example sampling scheme
The path over the Sensor Value describes the necessary functionality. Everything else is optional. The sampling scheme described from left to right is performing the following functionality:
- In the first path, triggered by either a random time event or through the SensorTrigger, an alarm is generated and a form is started. Before generating the alarm, the application assesses the elapsed time since the previous alarm. This prevents too many triggers from occurring within a short time frame. In this example, if two triggers occur within 3600 seconds (1 hour), the second trigger is blocked by the green “Frequency Limit” block.
- The second path describes an optional feature. If the SensorTrigger detects that the sensor has disconnected (e.g. the sensor was left at home) the smartphone will trigger an alarm and send a different form – usually a reminder or alert that the smartphone is out of range. The minimum disconnection time (seconds) can be configured in the preferences of each algorithm. The default setting is 20 seconds.
- The third path describes an optional feature. If the SensorTrigger detects the sensor cannot start measuring, the smartphone will trigger an alarm. This notifies the user that measurement has stopped, and cannot be restarted. This can occur if the sensor has run out of battery, or if the sensor has been connected to a PC. After recharging the sensor, it cannot start a new measurement until the data of the old measurement is read out on the PC.
- The fourth path describes an optional feature. It stops the Sensor from recording on the 8th day at 14:00 o’clock. Note that once the measurement ceases, like in the previous case, it cannot record again until the existing data is read out on the PC.
# The Generic Sensor Value condition
The generic Sensor Value condition allow to define custom conditions based on sensor values. Based on these sensor conditions alarm can be triggered. Multiple Sensor Value conditions can be combined by aligning them after each other.
WARNING
Each Sensor Value block in the sampling will result in a configuration screen during the movisensXS study start on the smartphone to select and validate the sensor for this block and configure participant based parameters.
# Specialized algorithm blocks
The following chapter describes the various algorithms which are available. These algorithms were developed for specific research projects and as such, represent a small selection of what is possible with this innovative technology. Please contact us if you would like your own algorithm implemented, or if you have a physiological marker that you would like to utilise as a trigger. We can work with you to develop an algorithm and design an appropriate study framework.
# Activity/Inactivity Algorithm
This algorithm detects periods of high and low activity in the participant. The algorithm is described in detail in the following publication:
Ebner-Priemer, Ulrich W., et al. "Interactive multimodal ambulatory monitoring to investigate the association between physical activity and affect." Frontiers in psychology 3 (2012)
The algorithm functions interactively, recalibrating itself based on the number of triggers sent in relation to activity and inactivity. It gradually adapts the activity thresholds to learn the higher and lower levels of each individual participant.
To do this, it assesses the volume of triggers and gradually adjusts the parameters outwards or inwards to achieve a higher triggering accuracy. Sleep periods are user defined to prevent inappropriate triggers from occurring.
# Sensor Requirements
To use the Activity/Inactivity algorithm, the Activity Sensor Move 3 or Move 4 is required.
# Configuration Parameters
Label | Description | Type | Default-Value |
---|---|---|---|
Activity Aggregation Length | Aggregation length for moving average filter in [min] | Integer | 10 |
Activity Threshold Start | Start value of activity threshold in [g] | Double | 0.2 |
Inactivity Threshold Start | Start value of inactivity threshold in [g] | Double | 0.01 |
Trigger Timeout | Trigger timeout, when no activity/inactivity trigger occurs, in [min] | Integer | 100 |
Time Between Triggers | Minimal time between triggers in [min] | Integer | 40 |
Threshold Change | Change of activity and inactivity thresholds in [%] | Double | 5.0 |
Upper Activity Inactivity Ratio Limit | Upper Activity Inactivity Ratio Limit | Double | 2.05 |
Lower Activity Inactivity Ratio Limit | Lower Activity Inactivity Ratio Limit | Double | 0.95 |
Minimal Triggers for Ratio Adaption | Minimal number of triggers before threshold adjustment starts | Integer | 6 |
# Algorithm Description
The input for the algorithm is the Movement Acceleration [g], sample rate 1/min, which is calculated on the sensor and transmitted once per minute
On the smartphone the following steps are performed:
- Aggregation over 10min segments (moving average).
- Trigger if:
- Threshold of activity is surpassed AND no sleeping time AND last trigger was sent during the last 40 to 100 min
- Threshold of inactivity is reached AND no sleeping time AND last trigger was sent during the last 40 to 100 min
- the last trigger was sent 100 min ago
- Store occurrence of triggers
- Calculate the ratio between inactivity and activity episode-based triggers
- Change thresholds IF there have been at least six triggers
- Increase thresholds for activity and inactivity by 5% IF activity/inactivity trigger ratio is above 2.05
- Decrease thresholds for activity and inactivity by 5% IF activity/inactivity trigger ratio is below 0.95
- the threshold for activity episodes is decreased by 5% and the threshold for inactivity episodes is increased by 5% IF the current trigger is a time-limit trigger
# Simple EDA Scl Mean Algorithm
This basic algorithm uses changes in the skin conductance level (a good indicator of emotional arousal) to trigger a form in movisensXS. The EdaMove 3 or EdaMove 4 transmits an EdaSCL mean value every thirty seconds to the smartphone. These values are averaged after a minute and compared to the previous minute. When the value differs by the user defined percentage, a form can be triggered within movisensXS.
# Sensor Requirements
To use the Simple EDA Scl Mean algorithm, the EDA and Activity sensor EdaMove 3 or EdaMove 4 is required.
# Configuration Parameters
Label | Description | Type | Default-Value |
---|---|---|---|
Eda Threshold (%) | Percentual change of last value should trigger movisensXS | Integer | 80 |
# Algorithm Description
- LastMinuteMean * percentage/100 < abs(LastMinuteMean - ActualMinuteMean) -> Trigger
- LastMinuteMean = The mean SCL value from the last minute
- ActualMinuteMean = The current SCL mean value
# Sedentary Algorithm
This algorithm allows to trigger on phases of sedentaryness. It is based on the bodyposition parameter. You can set the minimum sedentary duration.
# Sensor Requirements
To use the Sedentary algorithm, the Activity Sensor Move 3 or Move 4 is required. The sensor has to be worn on the sensor location thigh. Other sensor locations do not allow for accurate differentiation between standing vs. sitting/lying. Make sure the sensor location is set correctly when coupling the sensor in the movisensXS app.
Please also check our sendentary vs. mood study decription: Part 1 (opens new window), Part 2 (opens new window), Part 3 (opens new window), Part 4 (opens new window), Part 5 (opens new window), Part 6 (opens new window), Part 7 (opens new window).
# Configuration Parameters
Label | Description | Type | Default-Value |
---|---|---|---|
Minimum Duration [min] | Minimum duration in sedentary activity [min] | Integer | 30 |
# Algorithm Description
- The algorithm checks for a sedentary bodyposition (sitting/lying)
- After the defined minimum minutes of consecutive time in sedentary the blocks outout is set to true
# Additional Notes
- Alternatively you could use a Sensor Value Condition Block with the settings: BodyPosition equal sitting/lying.
# Literature and Validation
# Adaptive EDASCL Algorithm
The adaptive EdaScl trigger algorithm triggers on high Eda values. The algorithm uses two thresholds that get adapted continuously. One threshold is used during low physical activity and the other during high physical activity.
# Sensor requirements
To use the Adaptive EdaScl Algorithm, the EDA and Activity sensor EdaMove 3 or EdaMove 4 is required.
# Algorithm description
The Adaptive EdaScl Trigger is active when the incoming condition of the block is true. That means you place e.g. a time range block in front of the trigger block to define the sampling window (i.e. to activate and deactivate the algorithm).
The sampling window will be divided into subwindows. The algorithm will try to output one trigger per subwindow. You can specify a maximum duration without triggers. If no EdaScl trigger were found during this time a timeout trigger will be fired. The threshold for each subwindow will be calculated from the last third values of the previous subwindow. In the first subwindow of the first day no triggers will be fired.
# Threshold calculation
- After each subwindow: the last 3rd of the subwindow data is used to calculate the threshold.
- At first the EdaScl values are selected where the movement acceleration intensity conditions are met. Then a running mean filter is applied to EdaScl signal. The length of the filter is set by the parameter edaSclRunningMeanLength. The highest EdaScl value of the interval that meats the conditions is used as the threshold of next subwindow.
# Parameter description
Parameter | Default value | Description |
---|---|---|
subWindowSizeMinutes | 100 min | Defines the duration of a subwindow in which only one trigger can take place |
minimumTimeBetweenTriggersMinutes | 30 min | Defines the minimum distance between 2 triggers (condition) |
maximumTimeWihtoutTriggerMinutes | 150 min | In case there is no trigger found for the given duration, than regardless of the fulfillment of the conditions, a trigger is fired |
minConsecutiveEdaSclValuesUnderThreshold | 2 | Defines the minimum number of consecutive EdaScl values that have to be lower than the calculated threshold (condition) |
edaSclRunningMeanLength | 2 | Number of EdaScl values to be used for threshold calculation. |
enableHighActivityTriggers | true | Configures if there should be triggers fired also during high physical activity intervals. |
physicalActivityCutpoint | 0.05 g | Cutpoint that divides high physical activity from light/no activity |
minimumTimeoutAfterActivity | 5 min | Defines the minimum duration needed after a high activity to fire a low activity trigger. And the minimum duration of high activity to fire a high activity trigger (condition) |
thresholdTolerancePercent | 0.0 % | Actual threshold is lower than the ideal threshold by the given percentage |
# Additional Information
You can use a SensorValue block to remind a participant if the sensor is not worn.
If you want to suppress the internal timeout triggers set the maximumTimeWihtoutTriggerMinutes greater than the sampling window length.
# SimpleRmmssdAlgorithm
The Simple RMSSD algorithm is a basic auto-configuring version of the Decreased RMSSD algorithm. It lacks the customisation that is available in the Decreased RMSSD algorithm and uses periods of inactivity to estimate the RMSSD. The following figure shows an overview of the Simple RMSSD algorithm. The boxes in yellow show the configurable parameters. Further information on Heart Rate Variability and RMSSD is in the description of the Decreased RMSSD algorithm.
# Sensor requirements
To use the Adaptive Rmssd Algorithm, the ECG and Activity sensor EcgMove 3 or EcgMove 4 is required.
# Algorithm description
The inputs for the algorithm are:
HRV RMSSD [ms], sample rate 1/min
Movement Acceleration [g], sample rate 1/min
Activity Levels (inactive, sedentary, and active) are derived from Movement Acceleration. The following cut points are used (the selection of these cut points is based on measurements performed at movisens and KIT):
Inactive / Sedentary: 20 mg
Sedentary / Active: 200 mg
To be sure that the participant is in the desired activity level and HRV parameters have settled, cool down blocks are used and can be configured manually (Minimum duration in sedentary or inactivity [min], Minimum duration in sedentary [min]). RMSSD mean values are calculated when the participant is in the sedentary category. This assures that the RMSSD mean value is not too high or too low. This would be the case if RMSSD values in inactive or active levels were used. The RMSSD values acquired in inactive or sedentary level are then compared to a threshold, defined as a ratio between 0 and 1 (in the RMSSD ratio to trigger movisensX [0..1]), from the mean RMSSD value. If the RMSSD values are below the set threshold for a defined time period (minimum duration in low RMSSD) a trigger is sent to movisensXS.
# Parameter description
The following parameters can be customized for the simple rmssd algorithm:
- Minimum duration in sedentary or inactivity [min]: Default 10 minutes
- Minimum duration in sedentary activity [min]: Default 10 minutes
- Minimum duration in low rmssd [min]: Default 5 minutes
- RMSSD ratio to trigger movisensXS [0...1]: Default 0.7
# Adaptive Rmssd Algorithm
The adaptive RMSSD trigger algorithm triggers on low RMSSD values. It adapts thresholds continuously. The algorithm can use two thresholds one when there is no physical activity (low activity trigger) and a second threshold when the participant is physically active (high activity trigger).
# Sensor requirements
To use the Adaptive Rmssd Algorithm, the ECG and Activity sensor EcgMove 3 or EcgMove 4 is required.
# Algorithm description
The Adaptive RMSSD Trigger is active when the incoming condition of the block is true. That means you place e.g. a time range block in front of the trigger block to define the sampling window (i.e. to activate and deactivate the algorithm).
The sampling window will be divided into subwindows. The algorithm will try to output one trigger per subwindow. You can specify a minimum duration without triggers. If no RMSSD trigger were found during this time a timeout trigger will be fired. The thresholds for each subwindow will be calculated from the values of the previous subwindow. The last third of the data is taken to calculate the threshold. In the first subwindow of the first day no triggers will be fired.
# Threshold calculation
- After each subwindow: the last 3rd of the subwindow data is used to calculate the threshold.
- At first the rmssd values are selected where the physical activity conditions are met (configured by physicalActivityCutpoint and minimumTimeoutAfterActivity). Then a running mean filter is applied. The length of the filter is set by the parameter rmssdRunningMeanLength. The smallest rmssd value of the interval that meats the conditions is used as the threshold of next subwindow.
# Parameter description
Parameter | Default value | Description |
---|---|---|
subWindowSizeMinutes | 100 min | Defines the duration of a subwindow in which only one trigger can take place |
minimumTimeBetweenTriggersMinutes | 30 min | Defines the minimum distance between 2 triggers (condition) |
maximumTimeWihtoutTriggerMinutes | 150 min | In case there is no trigger found for the given duration, than regardless of the fulfillment of the conditions, a trigger is fired |
minConsecutiveRmssdValuesUnderThreshold | 2 | Defines the minimum number of consecutive rmssd values that have to be lower than the calculated threshold (condition) |
rmssdRunningMeanLength | 2 | Number of rmssd values to be used for threshold calculation. |
enableHighActivityTriggers | true | Configures if there should be triggers fired also during active intervals. |
physicalActivityCutpoint | 0.05 g | Cutpoint that divides high physical activity from light/no activity |
minimumTimeoutAfterActivity | 5 min | Defines the minimum duration needed after a high activity to fire a low activity trigger. And the minimum duration of high activity to fire a high activity trigger (condition) |
thresholdTolerancePercent | 20.0 % | Actual threshold is higher than the ideal threshold by the given percentage |
# Additional Information
You can use a SensorValue block to remind a participant if the sensor is not worn or if the signal quality is not good enough for HRV parameter calculation (Sensor Signal: Hrv Is Valid, has value: equal, to: false, for: at least, 5 Minutes).
If you want to suppress the internal timeout triggers set the maximumTimeWihtoutTriggerMinutes greater than the sampling window length.
The algorithm probably will not work for participants with heart arrythmia because heart rate variability is affected by non normal to normal heart beat intervals.
# DecreasedRmssdAlgorithm
The goal of this algorithm is to detect when psychological stress causes a derease in the heart reate variability parameter RMSSD and then trigger a questionnaire. The algorithm is based on Verkuil et al. 2016 (opens new window).
# Sensor requirements
To use the Decreased Rmssd Algorithm, the ECG and Activity sensor EcgMove 3 or EcgMove 4 is required.
# Algorithm description
Heart Rate Variability (HRV) is associated with physiological and psychological wellbeing of a person. Decreased HRV corresponds to physical and psychological stress, and baseline HRV indicates overall physical fitness. When engaged in intense physical exercise, HRV value decreases, and HRV can also decrease due to psychological stress. The goal with this algorithm is to detect when psychological stress causes this decreased value, and then trigger a questionnaire. HRV is measured and assessed using RMSSD (which is the square root of the mean of the squares of the successive differences in-between adjacent heart beats).
To sum up, this algorithm aims to increase the probability to trigger a questionnaire based on a psychologically caused decrease in RMSSD. It’s been used for studies assessing psychological stress isolated from stress related to physical activity. However, this algorithm also has some limitations.
- Firstly, to use this algorithm effectively, a pre-study must occur to capture the relevant parameters for each participant. (More on this point later.)
- Secondly, despite this data, the sensor can only isolate and remove the exertion effects from the body when it’s moving through space. The decreased state of RMSSD from any stationary exertion such as stationary cycling or lifting heavy objects will not be isolated and the corresponding period can be mistakenly interpreted as psychological stress.
Utilizing a logic chain within movisensXS to determine whether any physical exertion occurred at the time of the alarm can counteract this problem. Simply asking if the participant experienced physical exertion as the first question, and then making the following questions visible when “No” is selected achieves this goal.
The algorithm tracks periods of Decreased RMSSD activity, and then assesses and isolates the physical causes utilizing the following steps. When the algorithm concludes the cause of Low RMSSD is not from physical activity, a trigger is sent to movisensXS.
The data inputs for the algorithm are:
- HRV RMSSD [ms], sample rate 1/min
- HrvIsValid, sample rate 1/min
- Movement Acceleration [g], sample rate 1/min
It calculates the predicted average RMSSD level based on the maximum level of movement measured during the previous “Window length of Movement Acceleration [min]”.
This continuous prediction of RMSSD can take the form of one of these function:
- Predicted RMSSD = intercept + slope x movement (linear relation)
- Predicted RMSSD = intercept + slope / movement (inverse relation)
Values for regression method, intercept and slope for each individual have to be entered for each participant during coupling the study, using calibration data acquired during resting and exercise.
A RMSSD threshold can be defined, as a measurement error has to be taken into account. If the actual RMSSD falls below the Predicted RMSSD minus the threshold this minute is tagged as a “Period of Low RMSSD”.
- RMSSD for comparison = Predicted RMSSD - Threshold
- Threshold has to be configured for each participant during coupling the study
The algorithm keeps track of the number of periods (minutes) where the RMSSD is below the configured threshold. To do this, you configure the RMSSD Assessment Window - a window of time (in minutes) - and allocate the number of 1 minute periods in which the RMSSD should be below the threshold. For instance, over a 10 minute Window you may require 8 periods (minutes) of Low RMSSD – that is, RMSSD that is under the configured threshold. The Length of Window and the amount of Periods can be configured.
When the configured number of periods in Low RMSSD has been detected, a trigger is sent to movisensXS.
- The total amount of prompts indicating prolonged periods of low RMSSD per day must be configured in movisensXS
- The prompts indicating prolonged periods of Low RMSSD should be interspersed with randomly triggered ‘neutral prompts’ at which no “Low RMSSD” is present during the preceding 10 minutes.
- Minimum amount of time between the prompts is: 60 minutes.
When no valid RMSSD values are available then no trigger will be provided (HRV data not valid = No period of Low RMSSD).
The decreased RMSSD algorithm can be used to filter additional triggers that can be generated from other movisensXS blocks. That means you can e.g. have interspersed triggers when no low RMSSD trigger occurs. To send an additional trigger to the algorithm use a Send intent block with Action set to
rmssd.random
.
You can set the minimal time that is needed after a low RMSSD minute to accept an additional trigger. To determine which type of trigger was fired by the DecreasedRmssdAlgorithm, PRD will be shown for low RMSSD triggers and RND for interspersed triggers in the results file.The algorithm has two modes for triggering
- direct mode: as soon as the algorithm detects a prolonged time in Low RMSSD as described above
- externally triggered by mutable value: the algorithm keeps track of trigger events due to prolonged time in Low RMSSD in a history. The algorithm triggers only if it is externally triggered by a mutable value set to 1 and additionally having at least one trigger event in the history. History length can be defined.
# Parameter description
# Participant parameters
As previously noted, this algorithm requires a period of pre-study to obtain the individual RMSSD, Slope, and Intercept parameters of each participant. These parameters have to be entered after coupling a smartphone to the study:
Parameter | Description |
---|---|
Regression Parameter slope in [ms] | Slope used in the RMSSD prediction function |
Regression Parameter intercept [ms/mg] or [mg/ms] | Intercept used in the RMSSD prediction function |
Threshold for RMSSD in [ms] | Threshold used to determine if a minute is considered as low RMSSD |
# Study parameters
The following parameters can be set in the DereasedRmssd block in the sampling scheme:
Parameter | Default value | Description |
---|---|---|
Regression method | linear | Select linear or inversion prediciton function |
Window length of Movement Acceleration [min] | 5min | Window length to determine movement acceleration values for prediciton function (maximum) |
RMSSD Assessment window in [min] | 10min | Window length to be used for decreased RMSSD assessment |
No. of periods in Low RMSSD [min] | 8min | Number of periods in assessment window that have to be marked as decreased RMSSD to fire a trigger |
Minimal distance between prompts in [min] | 60min | Minimal time between two triggers sent from this block regardless of low RMSSD trigger or additional interspersed trigger |
Minimum time from last Low RMSSD [min] | 10min | If interspersed triggers are used, this parameter defines the minimal time for a interspersed trigger after a low RMSSD minute |
Mutable Value for External Triggering | empty | If no mutable value is set, the algorithm runs in direct mode. If a mutable value is set, the algorithm runs in externally triggered by mutable value mode. The mutable value has to be set to 1 by a change mutable value block. Then the events in the history will be evaluated and the mutable value will be reset to 0. |
History Length | 20min | Defines the length (in min) of the history which holds trigger events |
# Literature
# All sensor sampling blocks
Sensors
Activity/Inactivity On request
Triggers when periods of activity/inactivity are detected.
Label | Description | Type | Default-Value |
---|---|---|---|
Activity Aggregation Length On request | Aggregation length for moving average filter in [min] | Integer | 10 |
Activity Threshold Start On request | Start value of activity threshold in [g] | Double | 0.2 |
Inactivity Threshold Start On request | Start value of inactivity threshold in [g] | Double | 0.01 |
Trigger Timeout On request | Trigger timeout, when no activity/inactivity trigger occurs, in [min] | Integer | 100 |
Time Between Triggers On request | Minimal time between triggers in [min] | Integer | 40 |
Threshold Change On request | Change of activity and inactivity thresholds in [%] | Double | 5.0 |
Upper Activity Inactivity Ratio Limit On request | Upper Activity Inactivity Ratio Limit | Double | 2.05 |
Lower Activity Inactivity Ratio Limit On request | Lower Activity Inactivity Ratio Limit | Double | 0.95 |
Minimal Triggers for Ratio Adaption On request | Minimal number of triggers before threshold adjustment starts | Integer | 6 |
Adaptive Eda Scl On request
Adaptive Eda Scl.
Label | Description | Type | Default-Value |
---|---|---|---|
Sub Window Size On request | Sub Window Size in [min] | Integer | 100 |
Minimum Time Between Triggers On request | Minimum Time Between Triggers in [min] | Integer | 30 |
Maximum Time Without Trigger On request | Maximum Time Without Trigger in [min] | Integer | 150 |
Minimum Consecutive Eda Scl Values under Threshold On request | Minimum Consecutive Eda Scl Values under Threshold in [min] | Integer | 2 |
Eda Scl Running Mean Length On request | Eda Scl Running Mean Length | Integer | 2 |
Physical Activity Cutpoint On request | Physical Activity Cutpoint | Double | 0.05 |
Minimum Timeout After Activity On request | Minimum Timeout After Activity | Integer | 5 |
Threshold Tolerance Percent On request | thresholdTolerancePercent | Double | 20.0 |
Enable High Activity Triggers On request | Enable High Activity Triggers | Boolean | true |
Adaptive Rmssd On request
Adaptive Rmssd Algorithm.
Label | Description | Type | Default-Value |
---|---|---|---|
Sub Window Size On request | Sub Window Size in [min] | Integer | 100 |
Minimum Time Between Triggers On request | Minimum Time Between Triggers in [min] | Integer | 30 |
Maximum Time Without Trigger On request | Maximum Time Without Trigger in [min] | Integer | 150 |
Minimum Consecutive Rmssd Values under Threshold On request | Minimum Consecutive Rmssd Values under Threshold in [min] | Integer | 2 |
Rmssd Running Mean Length On request | Rmssd Running Mean Length | Integer | 2 |
Physical Activity Cutpoint On request | Physical Activity Cutpoint | Double | 0.05 |
Minimum Timeout After Activity On request | Minimum Timeout After Activity | Integer | 5 |
Threshold Tolerance Percent On request | thresholdTolerancePercent | Double | 20.0 |
Enable High Activity Triggers On request | Enable High Activity Triggers | Boolean | true |
Decreased Rmssd On request
Triggers when stress periods causing a decrease of the rmssd.
Label | Description | Type | Default-Value |
---|---|---|---|
Window length of Movement Acceleration [min] On request | Window length of Movement Acceleration [min] | Integer | 5 |
Regression method On request | Regression method [LINEAR, INVERSE] | String | LINEAR |
RMSSD Assessment Window [min] On request | RMSSD Assessment Window [min] | Integer | 10 |
No. of periods in Low RMSSD [min] On request | No. of periods in Low RMSSD [min] | Integer | 8 |
Minimal distance between prompts [min] On request | Minimal distance between prompts [min] | Integer | 60 |
Minimum time from last Low RMSSD [min] On request | Minimum time from last Low RMSSD [min] | Integer | 10 |
Mutable Value for External Triggering On request | Mutable value name for external triggering. | String | |
History Length On request | History length in external triggering mode. | Integer | 20 |
Eda Scl Steps On request
Triggers when eda scl rises considering step count increase.
Heart Rate On request
Triggers when heart rate reaches specified threshold (specified at study start)
Log Sensor Connection State On request
Logs connection states of coupled movisens sensors.
MET Level On request
Triggers when the 'Metabolic Equivalent of task level Algorithm' triggers
Movement Acceleration On request
Triggers when the movement acceleration reaches specified threshold.
Label | Description | Type | Default-Value |
---|---|---|---|
Movement Acceleration Threshold [g] On request | Threshold of movement acceleration | Double | 0.2 |
Sensor Battery Low On request
Triggers when one of the coupled sensors has a low battery.
Label | Description | Type | Default-Value |
---|---|---|---|
Battery Level On request | Battery threshold. | Integer | 30 |
Sensor Disconnected On request
Triggers when one of the coupled sensors disconnects from the smartphone.
Label | Description | Type | Default-Value |
---|---|---|---|
Trigger Time On request | Time to trigger after connection loss (in s). | Integer | 60 |
Sensor Paused On request
Triggers when one of the coupled sensors is in an paused state.
Sensor Stopped On request
Triggers when one of the coupled sensors stops recording.
Sensor Value On request
Compares sensor values against simple thresholds, values, ranges or percentage changes.
Label | Description | Type | Default-Value |
---|---|---|---|
Movisens Characteristic On request | Characteristic or parameter to assess | String | Movement Acceleration |
Absolute or Percentage On request | Defines if borders are absolute values or percentage changes | String | absolute |
Comparison Operator On request | Compares operators between sensor value and comparison value. E.g. is true if sensor value greater than comparison value | String | equal |
1st Compare Value On request | First comparison value to activate the condition | Double | 0 |
2nd Compare Value On request | Second comparison value to activate the condition. Only used for inside/outside range. | Double | 0 |
Temporal Operator On request | Temporal operator which determines if the proposition should be true X times in a row, or at least once in X minutes. | String | at least |
Temporal Value On request | Temporal value related to the temporal operator | Integer | 1 |
Calculation Type On request | Defines which calculation type will be used in window. | String | sum |
Sedentary On request
Triggers when participant is sitting for a configured time span.
Label | Description | Type | Default-Value |
---|---|---|---|
Minimum Duration [min] On request | Minimum duration in sedentary activity [min] | Integer | 30 |
Simple Eda Scl Mean On request
Triggers when 'eda scl mean' differs from previous value with a given threshold percentage.
Label | Description | Type | Default-Value |
---|---|---|---|
Eda Threshold (%) On request | Percentual change of last value should trigger movisensXS | Integer | 80 |
Simple Rmssd On request
Triggers when rmssd value is below a specific threshold.
Label | Description | Type | Default-Value |
---|---|---|---|
Settle After Active Duration On request | Settling duration after active period in [min] | Integer | 10 |
Minimum Duration in Low Rmssd On request | Minimum duration of low RMSSD state for triggering in [min] | Integer | 5 |
Rmssd Ratio On request | Ratio between low and baseline RMSSD | Double | 0.7 |
Stop Sensor On request
This action stops all coupled movisens sensors
Write Current Time to Sensor On request
This action will write the current time to all connected times