InclusiveHAR: A Smartphone-Based Dataset for Human Activity Recognition Across Diverse Physical Abilities

InclusiveHAR is a multivariate time-series dataset designed for human activity recognition using wearable smartphone sensors, with a focus on individuals with physical disabilities. The dataset includes data collected from 20 participants, consisting of 10 non-disabled individuals and 10 individuals with disabilities.

Data were recorded using an iPhone 14 Pro smartphone worn vertically in a waist pouch. Motion and location signals were captured from the device’s internal sensors, including accelerometer, gyroscope, magnetometer, motion sensors, and GPS-derived data, at a sampling rate of 50 Hz. The SensorLog app was used to lock the rate at 50 Hz.

Participants performed six daily activities: walking, standing, sitting, jogging, ramp ascent, and ramp descent. For safety reasons, the ramp ascent and ramp descent activities were conducted on an inclined ramp with an 8% slope. Each activity was repeated multiple times to ensure consistent recordings. Additionally, "walking" labels for wheelchair users refer to manual propulsion.

The dataset is organized as a time-series table in which each row corresponds to a single time-step. It contains 30 feature columns representing sensor signals, along with an activity label column, a binary disabled indicator where 0 denotes non-disabled participants, and 1 denotes participants with disabilities, and a UserID column identifying each participant.

InclusiveHAR is intended to support research on machine learning and deep learning methods for human activity recognition, particularly in healthcare, rehabilitation, and assistive technology applications. All participants provided informed consent, and data collection was conducted under professional supervision with strict attention to safety and privacy. Additionally, detailed descriptions of individuals with disabilities have been provided as supplementary material.

InclusiveHAR是一款专为基于可穿戴智能手机传感器的人类活动识别（Human Activity Recognition, HAR）任务设计的多变量时间序列数据集，其研究重点聚焦于肢体残障人群。该数据集收录20名受试者的采集数据，其中10名为无肢体障碍者，10名为肢体残障人士。
数据采集时，受试者将iPhone 14 Pro智能手机垂直放置于腰部收纳袋中。设备内置传感器采集运动与位置信号，包括加速度计、陀螺仪、磁力计、运动传感器以及GPS衍生数据，采样率设定为50 Hz，通过SensorLog应用将采样速率锁定至该值。
受试者完成六项日常活动：行走、站立、坐立、慢跑、坡道上行与坡道下行。出于安全考量，坡道上行与下行活动均在坡度为8%的倾斜坡道上开展。每项活动均重复多次以保证采集数据的一致性。此外，轮椅使用者的“行走”标签指代手动轮椅推进动作。
该数据集以时间序列表格形式组织，每一行对应单个时间步。数据集包含30个表征传感器信号的特征列，同时设有活动标签列、二分类残障标识列（0代表无残障受试者，1代表残障受试者）以及用于标识每位受试者的UserID列。
InclusiveHAR旨在支持面向人类活动识别的机器学习与深度学习方法研究，尤其适用于医疗保健、康复治疗以及辅助技术相关应用场景。所有受试者均签署了知情同意书，数据采集工作在专业监督下开展，严格遵循安全与隐私保护规范。此外，数据集附带补充材料，其中详细说明了每位残障受试者的具体情况。