SINEW 15 2023

Citation Author(s):
Ah-Hwee
TAN
Budhitama
SUBAGDJA
Weng Yan
YING
Shanthoshigaa
D
Iris
Rawtaer
Submitted by:
wengyan ying
Last updated:
Mon, 11/25/2024 - 04:07
DOI:
10.21227/4haa-8t24
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Abstract 

The SINEW 15 2023 Biomarker dataset was extracted from the sensor data collected by a longitudinal study called Sensors IN-home for Elder Wellbeing (SINEW).

As the name suggested, the SINEW 15 2023 Biomarker dataset consists of 15 high-level biomarker features, derived from raw sensor readings. They cover four basic aspects of daily living, namely physical, activity, cognitive, and sleep, of each participant over a specific period. The digital biomarkers are of significance, as they can be used for early detection of mild cognitive impairment (MCI), providing an opportunity for timely intervention before it progresses to Alzheimer's disease.

For the purpose of training and evaluating machine-learning-based predictive models for MCI detection, each of the biomarker records is tagged with the cognitive status - either mild cognitive impairment (MCI) or normal cognition (NC) according to the participant's closest clinical assessments within a six month period from the date the sensor data are captured.

Altogether, three data sets are provided in this release, namely

SINEW 15 2023 Daily Biomarker data set, consisting of records of daily biomarker feature values tagged with the corresponding cognitive status labels;
SINEW 15 2023 Weekly Biomarker data set, consisting of records of weekly averaged biomarker values tagged with the corresponding cognitive status labels; and
SINEW 15  2023 Monthly Biomarker data set, consisting of records of monthly averaged biomarker values tagged with the corresponding cognitive status labels.

Each record/row in the three biomarker data sets contains 15 biomarker feature values as inputs and a cognitive class label (MCI vs NC) as outputs.

Instructions: 

Download csv file.

Funding Agency: 
National Medical Research Council , Singapore Management University
Grant Number: 
TA22jul-0012 , SAJL-2022-HAS001