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Actigraphy-analysis-project

   
Actigraphy analysis project
Predictive algorithms of individual wrist actigraphy for real life.


May 2021 Pre print.
delucca, gianluigi. 2021. “Motionwatch8 Wrist Actimetry Data Analysis: From Ambulatory Recording to Real Life Monitoring.” OSF Preprints. May 13. doi:10.31219/osf.io/pdctj.  Available also here.

March April 2021  Co recording of  Core temperature, Wrist actigraphy and HR in order to document the winter-summer time change. From Thursday March 25 until Tuesday April 6. Unfortunately the HR recording was not good.

At the end of 2020, the continous one second wrist actigraphy recording is stopped.  There are now 5 years of data that are a meaningful starting point for the analysis that was planned in 2015.

July 2020 -  Raw data of four months of continous one channel EKG in real life are available,  as described here.  

July 2019 - Expanded article "Wrist Actigraphy Analysis From Motionwatch8 Data" http://www.thinkmind.org/index.php?view=article&articleid=lifsci_v11_n12_2019_8
June 2019 - Start of the 4th year of one second epoch recording
September 2018 - SensorDevice2018. Conference presentation on Phase 2&3 http://www.thinkmind.org/index.php?view=article&articleid=sensordevices_2018_10_40_28007 and Award.
August 2018 - Raw data of Subproject 1- Phase 2&3 - are available at https://sleepdata.org/datasets/oya
August 2017  - Some additional details:  First year report - Phase I & II
January 2017 - While dataset develops, some rough evaluations are possible: Project PhaseIII report


 
The project 2015 .
This project is looking for new ways of sleep/circadian data analysis.  
The main target is to search for predictive algorithms from wrist actigraphy recordings of one subject using a MotionWatch8 (CamNtech Ltd) system on the non predominant wrist, for unrestricted lifestyle.  

The context
Wrist actigraphy has been used in the past 30 years to monitor motion activity.  
The actigraphy data have been accepted for the analysis of sleep of a single night, with a quantification of sleep duration and its fragmentation.
They are also used to check the presence of fluctuations in the circadian cycle, especially to highlight pathologies with shifts of falling asleep timing.
During the day it is possible to quantify exercise and (with calibration) recognize type and intensity of the exercise.

Continuous monitoring of wrist actigraphy in real life is possible and it is a valuable tool.
Nowadays, the needed hardware is cheap and it is possible to imagine useful applications in real life, for continuous monitoring and smart homes.
There are dozens of startups with wearable monitors based on actigraphy that use more or less the same parameters for recording and analysis of one minute epochs. And then, they make the comparison against a(nother) so called "normal" group they built for the occasion, frozen in time and space.

New models and algorithms need long term data sets and guidelines on methodological issues. It seems that both are not easily available, on and off line.  

Some examples are available in partially controlled environments:
- Nearly 600 days in 2 patients
Werth, Esther, Egemen Savaskan, Vera Knoblauch, Paola Fontana Gasio, Eus J.W. van Someren, Christoph Hock, Anna Wirz-Justice. Decline in long-term circadian rest-activity cycle organization in a patient with dementia. J Geriatr Psychiatry Neurol, 2002; Vol. 15; pp. 55-59.
- 50 days and then 30 days after 5 months
Miller, Nita Lewis, Shattuck, Lawrence, G. Sleep Patterns of Young Men and Women Enrolled at the United States military Academy: Results from Year 1 of a 4-Year Longitudinal Study. Sleep, 2005; Vol. 28; No. 7; p. 837.
- One month twice a year for 4 years
Longitudinal Study of Sleep Patterns of United States Military Academy Cadets
Nita Lewis Miller, Lawrence G. Shattuck, Panagiotis Matsangas
Sleep. 2010 December 1; 33(12): 1623–1631.  
- 6 months sea duty
Nita Lewis Shattuck ; Panagiotis Matsangas
A 6-Month Assessment of Sleep During Naval Deployment: A Case Study of a Commanding Officer. Aerospace medicine and human performance Vol. 86, No. 5 May 2015

Few recordings are available in free life:
- 5 months, one patient
Garbazza C, Bromundt V, Eckert A,Brunner DP, Meier F, Hackethal S and Cajochen C (2016)  
Non-24-Hour Sleep-Wake Disorder Revisited – A Case Study.
Front. Neurol. 7:17. doi: 10.3389/fneur.2016.00017
- 4 months, 80 OSA patients and 50 controls.
Sleep remains disturbed in patients with obstructive sleep apnea treated with positive airway pressure: a three-month cohort study using continuous actigraphy  
Sleep Medicine, Volume 24, August 2016, Pages 24-31 Jon Tippin, Nazan Aksan, Jeffrey Dawson, Steven W. Anderson, Matthew Rizzo
- And the amazing 30 years!
ESRS 2016 Bologna
P041 Three decades of continuous motor activity recording:analysis of sleep duration
A. Borbely, T. Rusterholz and P. Achermann Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
Objectives: Motor activity recording by a wrist-worn device is a common unobtrusive method to monitor the rest-activity cycle. We present a first analysis of data that have been obtained over more than three decades.


To answer to the need of a suitable data set, I started one long term recording on myself, late 2015. Since it is expected from physiology to find rhythms with a period long at least one year, a recording with a minimum length of two years is today the first target.

The goal
Existing analysis methodologies for wrist actigraphy, describe the recorded data but do not provide any personal parameter, I mean something that alert or reassures about your body status, at least for the coming day, as well as we do for Temperature or Blood pressure measurements.
On the contrary, I think that for personal monitors we need personal parameters, that "grow old" with the subject, in his own way.
I think that for such parameters, we need to create data models and develop algorithms that use Artificial Intelligence.
Hopefully, the analysis of the long term, high sampling data of  subproject 1 will allow researchers to find at least one parameter that will be possible to use. Anyway, even if that search fails, the need of the one second epoch in this field of studies is bubbling, and that recording will be useful.
The possible analysis will be tied to the lenght of the recording, but also on the models that will be invented for this new type of recording. It is something that need more than one research center and therefore the idea to make the first year at one second epoch (Phase 2 and 3) available to the public.

A detailed data log    of  both Subproject 1- Dataset  and  Subproject 2- Dataset  is available here.


Additional information
For those not already interested in actigraphy, there is an introduction to the reasons why I find the subject interesting. Could be suitable for an introductory speech for students.
There is a .ppt file Download and related .doc Download
In the .ppt, I left out the part relating to the detection of light because, at this level, I think that conceptually it does not add anything.

I divide the issue in 3 main areas:
a) Automatics
How to model the situation in which a sensor is located at the far end of a jointed limb of a body in motion with a) limb movement b) movement of the body isolated c) movement of the body from interaction not known d) with position sensor not constant.
Out of past experiences, wrist recording was useful for sleep analysis. In the model we may add, for that situation, the interaction with the mattress and/or the horizontal position.
That could bring modelling of the expected effect, on the recordings, of different sleeping positions.

b) Mathematics
Topic: Transition from a curve in space-time to a discrete, filtered, one-dimensional numerical series.
Probably it does exists a specific area of  Mathematics for that, but I would not know which one.
For actigraphy there are important practical consequences.
While movement recording is more and more widespread due to technological advances, each manufacturer uses his own raw data transformation. Most of them do not declare it, not to mention to document, it.
For that reason, it is impossible to use numerical data out of different systems.
Therefore, each manufacturer of medical devices is providing his own software analysis of the data recorded. Since all of the resulting parameters are somehow validated against gold standard (still visual “readings” of polisomnographic recordings....), the results for that purpose are similar.
A more analytical way to compare raw data could allow better meta analysis of clinical studies that use different devices.

c) AI.  
Out of past experiences, we know that there is a correlation between body movements and body internal states. Which dynamics of internal states is theoretically possible to monitor and which are not, is an always open issue.
For the analysis, the data set reminds me situations close to ethology (recording parameters in a situation not controlled) and astronomy (discrete log of phenomena over long time scales), but o I have no experience of  processing of that type of data.
I hope that to offer data to the public may generate some contamination.



 
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