DOI: 10.5281/zenodo.17191996
title: ElderHAR: A Real-World Inertial Dataset for Elderly Activity Recognition

description: Background and Purpose:

Falls are a major health concern among older adults, often leading to disability, hospitalization, and loss of independence. Human Activity Recognition (HAR) using wearable sensors has emerged as a promising tool for continuous fall risk assessment, but existing public datasets largely rely on young, healthy participants and controlled laboratory tasks. This gap limits the development of models that generalize well to elderly populations and real-world conditions.

To address this need, we present ElderHAR, a dataset of continuous, real-world inertial recordings from 54 older adults performing daily activities and natural transitions across six care institutions. Collected with a single waist-mounted IMU, the dataset comprises over 2.6 million labeled samples, rich metadata, and supporting code for preprocessing and benchmarking. By emphasizing ecological validity and elderly-specific variability, ElderHAR provides a valuable resource for advancing HAR, fall risk assessment, and elderly care technologies.

 

Dataset Content:The dataset comprises approximately 15 hours of annotated human activity data, amounting to over 2.6 million samples of synchronized tri-axial accelerometer and gyroscope signals. Data were collected at a fixed sampling rate of 50 Hz and were stored in comma-separated values (CSV) format files. Each file corresponds to a single trial (a single continuous activity circuit) performed by a participant.

Each CSV file contains the following columns:



Column 1: Sample — sample count; 

Column 2: Subject — subject ID; 

Column 3: Trial — trial ID; 

Column 4-6: AccX, AccY, and AccZ — acceleration in the X, Y, and Z axes (in g); 

Column 7-9: GyrX, GyrY, and GyrZ — angular velocity in the X, Y, and Z axes (in °/s); and 

Column 10: Label — numeric code corresponding to the activity class.


A separate metadata CSV file is also provided, containing relevant information for each participant, including, when available: (i) subject ID, (ii) age, (iii) gender, (iv) height (cm), (v) weight (kg), (vi) abdominal circumference (cm), and (vii) faller status, indicated as Faller (F) or Non-Faller (NF).

Additional documentation is provided in an accompanying README.txt file located in the main dataset folder. This file includes essential information on the data acquisition protocol, sensor specifications, label ID-to-activity mapping, and subject metadata. It also offers guidance on data usage, including preprocessing steps and modeling considerations, to support researchers in effectively working with the dataset.

Files follow the naming convention SubjectID_TrialNumber.csv. For example, S12_T01.csv corresponds to the first trial of participant 12. Each subject performed only one trial.

Participants & Data Acquisition System:Fifty-four participants from six elderly care institutions agreed to participate in the data acquisition. The recruitment aimed to maximize the diversity in physical stature, mobility characteristics, and social backgrounds. To achieve this, participants were recruited from both public and private institutions, including individuals with different educational backgrounds, and efforts were made to balance gender representation, although a higher proportion of women was ultimately included. The inclusion criteria were: (i) community-dwelling and able to walk independently (FAC > 3), (ii) no severe cognitive impairment (MMSE > 15), and (iii) able to follow verbal instructions.

Clinicians and caregivers assessed potential participants using clinical scales to ensure eligibility. Demographic and physiological data, including age, height, weight, gender, abdominal circumference, and faller status, were recorded. For a subset of participants (n = 32) from three institutions, abdominal circumference was available, ranging from 83 cm to 125 cm, further illustrating the variability in body composition across the sample. Table 1 summarizes the main characteristics of the study population.Data were collected using an instrumented waistband, originally developed by our team as described by Gonçalves et al. (2018). This system included an LSM6DSOX inertial sensor (accelerometer ±16 g, gyroscope ±2000 º/s) integrated into a compact, lightweight module. The sensing unit was centrally located on the waistband, connected to a control unit powered by an STM32F4 Discovery board, which managed data collection at 50 Hz for all sessions. Data were stored on a USB drive and an HC06 Bluetooth module enabled communication with external devices.

The IMU was strategically placed on the lower back via a waistband to optimally capture trunk movements, essential for postural transitions, gait and other daily activities. This placement ensures high-quality data acquisition while maintaining participant comfort and minimizing interference with natural movements.

To facilitate data acquisition and labeling, two custom Android applications were developed: one for managing the acquisition process and another for annotate each activity start timestamp. The waistband fabric was selected to improve comfort and adjustability for diverse abdominal circumferences, ensuring suitability for all elderly participants during the data acquisition sessions.

Table 1 - Participants' demographic information




Participant
Age
Gender
Height
Weight
Faller Status


1
84
F
155
43.1
NF


2
89
F
146
80
NF


3
90
F
150
63
NF


4
84
F
161
67
NF


5
85
F
147
55.2
NF


6
82
F
161
50.5
NF


7
80
M
168
56.6
NF


8
86
F
153
69
NF


9
61
F
150
67.4
NF


10
56
M
160
70.8
NF


11
86
F
160
52.4
NF


12
89
M
171
56.4
NF


13
64
M
167
52.3
NF


14
66
F
155
52
F


15
80
F
160
67
F


16
80
F
160
67
F


17
80
F
160
67
F


18
72
M
163
72
F


19
87
F
163
101
F


20
80
F
160
67
F


21
74
F
155
59
F


22
68
F
157
60
F


23
80
F
160
67
F


24
71
F
150
65.8
F


25
80
F
160
67
F


26
74
F
158
63
F


27
74
F
158
63
F


28
84
F
160
67
F


29
84
F
160
67
F


30
73
M
162
65.7
NF


31
99
F
152
56
NF


32
75
F
156
49
NF


33
88
F
155
69
NF


34
71
F
155
69
NF


35
84
F
162
73.4
NF


36
93
M
165
72.7
NF


37
73
F
158
98.7
NF


38
55
M
158
71
F


39
77
F
155
81.6
NF


40
93
F
149
46.9
NF


41
84
F
151
60.6
F


42
79
F
155
97.5
NF


43
82
M
171
86.9
NF


44
82
F
153
42.7
F


45
95
F
156
50.9
F


46
78
M
164
78
NF


47
71
M
160
52
NF


48
55
M
158
71
F


49
79
M
152
60
NF


50
75
F
160
67
F


51
62
M
170
62
NF


52
84
M
170
77
NF


53
84
F
162
69
NF


54
77
F
160
67
F




 

Data Collection Methodology:

The experimental protocol was designed to integrate four essential components: (i) precise control over stimuli, (ii) high reproducibility of the experimental conditions, (iii) preservation of ecological validity, and (iv) promotion of real-world learning transfer.

Data were collected in a continuous circuit format, with real-world settings and without movement restrictions, ensuring that the captured behaviors were natural and reflective of typical daily living. The dataset includes nine key activities relevant for FRA: a) Walking, b) Standing, c) Sitting in a chair, d) Laying in bed, e) Walking upstairs, f) Walking downstairs, g) Picking objects from the ground, h) Reaching for objects at height (to standardize movement, all participants lifted their heels off the ground while fully extending one or both arms upward), and i) Turning while walking or standing. Additionally, four important transitions, namely sit-to-stand, lay-to-sit, and their respective reverses, were also performed by the participants during the proposed circuit, making a total of 13 activities recorded.

The protocol included: (i) a pre-defined circuit of activities of daily living (ADLs); (ii) a minimum duration of two minutes for each cyclical activity (e.g., walking) and static posture (e.g., standing); (iii) naturally shorter, non-time-bound transitions (e.g., sit-to-stand); (iv) activity start and end times manually labeled in real time by a test administrator using a synchronized smartphone app; and (v) flexibility to pause the circuit if participants showed fatigue, as well as to skip activities they were uncomfortable performing.

Throughout the circuit, the administrator provided clear verbal instructions to guide participants through each activity and transition — for example, “walk to the chair and sit down normally” or “pick up the object from the floor and stand back up” — as well as directional cues such as “turn right” or “walk towards the stairs” to adapt to each institution’s layout.Activity sequences and durations varied across institutions based on available space and layout, adding diversity to the dataset. 

 

Data Labeling and Synchronization

During data acquisition, a trained administrator used a custom smartphone application to manually mark the start of each activity in real-time. This app was synchronized with the inertial sensing unit via timestamp alignment, ensuring that all activity annotations were accurately time-stamped to the millisecond. Each of the 13 predefined activities — ranging from static postures to cyclical movements and transitions — was labeled in this manner as it was performed.

Following the acquisition sessions, the recorded sensor data were post-processed and annotated on a per-sample basis. Using the synchronized timestamps between the sensor logs and manual annotations, each sample in the dataset was assigned one of the 13 activity labels listed in Table 2. These include static postures (standing, sitting, lying), dynamic movements (walking, stair ascent/descent), object-related tasks (picking and reaching), directional changes (turning), and transitions (sit-to-stand, stand-to-sit, lying-to-sit, and sit-to-lying).

After the initial annotations, all labels were also manually adjusted to remove any potential deviations or errors introduced during data collection. This manual optimization was particularly important for transitional activities, such as sit-to-stand or stand-to-sit movements. The start and end times of these transitions were normalized across all participants to ensure consistency in the dataset. Given the absence of video recordings due to privacy reasons, the optimization was based entirely on sensor data. Although this approach presented challenges, it ensured that participants' privacy was maintained while still capturing high-quality, labeled activity data.

Table 2 - Labels and corresponding activities in the dataset




Label
ID
Corresponding Activity


Walking
1
Ground Walking


Stand
2
Quietly Standing


Sit
3
Quietly Sitting


Lying
4
Lying in bed


UpStairs
5
Walking Upstairs


DownStairs
6
Walking Downstairs


Pick
7
Pick objects from the Ground


Reach
8
Reach objects in Height


Turn
9
180º turning while walking/standing


Stand-to-Sit
10
Get up from chair


Sit-to-Stand
11
Sit in a chair


Lying-to-Sit
12
Get up from bed into sitting


Sit-to-Lying
13
Lying in bed from sitting




 
description: Human Activity Recognition (HAR) is essential for health monitoring, rehabilitation, and fall risk assessment, supporting early detection of mobility decline in aging populations. Achieving reliable HAR requires datasets that capture realistic behaviors, diverse activities, and representative populations. However, publicly available resources rarely reflect the daily movement patterns of older adults in natural settings. This work presents a dataset collected from 54 elderly participants across six care institutions using a single, waist-worn inertial measurement unit (IMU). Participants performed 13 daily and transitional activities—including walking, sitting, lying, turning, and stair navigation—captured continuously without movement restrictions. The dataset comprises over 2.6 million synchronized tri-axial accelerometer and gyroscope samples at 50 Hz. Activity labeling was performed in real time via a synchronized smartphone app and refined offline for precision. Exploratory data analysis confirmed expected signal distributions, and baseline modeling with a lightweight 1D CNN achieved strong performance (89.5% accuracy, 90.0% weighted F1-score). By combining ecological validity, sensor simplicity, and a substantial elderly cohort, this dataset addresses a key gap in HAR research. All data, labels, and metadata are openly available to support reproducibility and the development of practical AI models for continuous monitoring and fall risk detection.
description: The respective article to this dataset is published in IEEE Data Descriptions. We kindly request that researchers using this dataset cite our paper as follows:

Citation:

L. Martins, N. F. Ribeiro, J. Cerqueira and C. P. Santos, "Descriptor: Elderly Human Activity Recognition Dataset (ElderHAR)," in IEEE Data Descriptions, vol. 3, pp. 54-62, 2026, doi: 10.1109/IEEEDATA.2025.3642775.

For those interested in further information about our research, please feel free to reach out to us via email [marquesmartins.luis@gmail.com, nuno.fribeiro@dei.uminho.pt or cristina@dei.uminho.pt]. Additional details about our projects and team can be found on our lab’s website: http://birdlab.dei.uminho.pt