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TIHM-Dataset

DOI

TIHM: An open dataset for remote healthcare monitoring in dementia. The dataset is available on its corresponding Zenodo repository. The full description of this dataset is published in Nature Scientific Data: paper

The dataset is provided for research purposes and supporting patient care.

Please acknowledge the Surrey and Borders Partnership NHS Foundation Trust and Howz in any publication or use of this dataset.

Summary of Data Records

The dataset is organised in five separate tables stored as separate CSV files, including, Activity, Sleep, Physiology, Labels and Demographics. Data can be cross-referenced across the files.

Activity

Value Type Number of Values Description
patient_id CategoricalDtype 56 hash code
location_name CategoricalDtype 8 Hallway,Lounge,Fridge Door,Bedroom,Kitchen,etc.
date dtype[datetime64] N/A from 2019-04-01 to 2019-06-30

Labels

Value Type Number of Values Description
patient_id CategoricalDtype 49 hash code
date dtype[datetime64] N/A from 2019-04-04 to 2019-06-30
type CategoricalDtype 6 Agitation,Body temperature,Weight,etc.

Physiology

Value Type Number of Values Description
patient_id CategoricalDtype 55 hash code
date dtype[datetime64] N/A from 2019-04-01 to 2019-06-30
device_type CategoricalDtype 8 Skin Temperature,Diastolic blood pressure,Heart rate,O/E - muscle mass,etc.
value dtype[float64] N/A min: 0.0, max: 211.0
unit CategoricalDtype 5 %,kg,mm[Hg],beats/min,etc.

Sleep

Value Type Number of Values Description
patient_id CategoricalDtype 17 hash code
date dtype[datetime64] N/A from 2019-04-01 to 2019-06-30
state CategoricalDtype 4 LIGHT,AWAKE,DEEP,REM
heart_rate dtype[float64] N/A min: 37.0, max: 107.0
respiratory_rate dtype[float64] N/A min: 8.0, max: 31.0
snoring dtype[bool] 2 True or False

Demographics

Value Type Number of Values Description
patient_id CategoricalDtype 56 hash code
sex CategoricalDtype 2 Male, Female
age CategoricalDtype 3 (70, 80],(80, 90],(90, 110]

Running the code

We have provided raw data and guidelines on how to access, visualise, manipulate and predict health-related events within the dataset. The Jupyter Notebooks have been developed using Python 3.9. For reproducing the code, an Anaconda virtual environment is also included. The virtual environment can be created using the following line of code in the Anaconda Terminal:

conda env create -f tihm.yml

After creating and activating the virtual environment, each notebook can be run individually. Please be careful to change the DPATH variable in each notebook with the folder in which the dataset has been downloaded.

DPATH = '../Dataset/'

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