Dataset Information

Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data.

ABSTRACT: With the emergence of machine learning for the classification of sleep and other human behaviors from accelerometer data, the need for correctly annotated data is higher than ever. We present and evaluate a novel method for the manual annotation of in-bed periods in accelerometer data using the open-source software Audacity®, and we compare the method to the EEG-based sleep monitoring device Zmachine® Insight+ and self-reported sleep diaries. For evaluating the manual annotation method, we calculated the inter- and intra-rater agreement and agreement with Zmachine and sleep diaries using interclass correlation coefficients and Bland-Altman analysis. Our results showed excellent inter- and intra-rater agreement and excellent agreement with Zmachine and sleep diaries. The Bland-Altman limits of agreement were generally around ±30 min for the comparison between the manual annotation and the Zmachine timestamps for the in-bed period. Moreover, the mean bias was minuscule. We conclude that the manual annotation method presented is a viable option for annotating in-bed periods in accelerometer data, which will further qualify datasets without labeling or sleep records.

SUBMITTER: Skovgaard EL

PROVIDER: S-EPMC8707394 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data.

Skovgaard Esben Lykke EL Pedersen Jesper J Møller Niels Christian NC Grøntved Anders A Brønd Jan Christian JC

Sensors (Basel, Switzerland) 20211217 24

With the emergence of machine learning for the classification of sleep and other human behaviors from accelerometer data, the need for correctly annotated data is higher than ever. We present and evaluate a novel method for the manual annotation of in-bed periods in accelerometer data using the open-source software Audacity<sup>®</sup>, and we compare the method to the EEG-based sleep monitoring device Zmachine<sup>®</sup> Insight+ and self-reported sleep diaries. For evaluating the manual annot ...[more]

PMID: 34960533

Similar Datasets

Project description:BackgroundMany large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.MethodsMeasurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation.ResultsMean TEE and AEE derived from DLW were 11.6 (2.3) MJ day-1 and 49.8 (16.3) kJ day-1 kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day-1 kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day-1 kg-1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day-1, r ~ 0.90).ConclusionsIn UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Dataset Information

Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data.

Publications

Manual Annotation of Time in Bed Using Free-Living Recordings of Accelerometry Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets