## 6 HAR Datasets with Physiological Data  
The availability of high-quality datasets is important for developing and evaluating any HAR model. Traditionally, HAR relies on motion data from wearable inertial or ambient sensors or image data from vision sensors. Some of the notable datasets are Weizmann [95], KTH Action dataset [96], Kinect Activity Recognition Dataset [97], Cornell Activity Dataset [98], MSR Daily Activity 3D Dataset [99], UCF50 [100], HDM05 [101], NTU RGBD [102], NTU RGBD-120 [103], Northwestern-UCLA, Human 3.6M [104], CMU Mocap, WISDM [105], Daily Human Activity (DHA), ARAS [106], CASAS, OPPORTUNITY [107], and UCI HAR [108]. However, research has shown that physiological data can improve the accuracy of HAR models [75], especially when applied to healthcare use cases. While numerous datasets for HAR with motion data exist, datasets incorporating physiological data are relatively limited. While these sensors offer precious insight into movement patterns, they fail to capture the physiological and contextual aspects of human activities [17]. Researchers have recently recognised this limitation and started integrating health vitals with traditional motion data. This section will review selected HAR datasets with physiological data incorporated. These datasets include PAMAP2 [109], ETRI lifelog [17], mHealth [110], EMG physical action, 19 NonSense, and Wrist PPG During Exercise [81] datasets. PAMAP2 was introduced as a new dataset for physical activity monitoring and benchmarked on several classification tasks [109]. The dataset contains over 10 h of data collected from 9 subjects (8 males and 1 female) performing 18 activities. The activities included 12 protocol activities (lie, sit, stand, walk, run, cycle, Nordic walk, iron, vacuum clean, rope jump, and ascend and descend stairs) and 6 optional activities (watch TV, computer work, drive car, fold laundry, clean house, and play soccer) [109]. Three Inertial Measurement Units (IMUs) and a heart rate monitor were used to capture data. The IMUs were attached with straps on the dominant arm and ankle and the third one was attached with the heart rate monitor on the chest. The dataset was made publicly available on the internet. ETRI lifelog dataset was collected from 22 participants (13 males and 9 females) over 28 days using a variety of sensors, including smartphones, wrist-worn health trackers, and sleep-quality monitoring sensors [17]. The dataset includes physiological data (photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature), behavioural data (accelerometer, GPS, and audio) and self-reported labels of emotional states and sleep quality. The authors demonstrated the feasibility of the dataset by using it to recognise human activities and extract daily behaviour patterns [17]. They suggested that the dataset can be used to understand the multifaceted nature of human behaviour and its relationship to physiological, emotional, and environmental factors. The dataset contains over 2.26 TB of data with 590 days of sleep quality data and 10,000 h of sensor data. The mHealth dataset includes body motion and vital signs recordings from 10 participants performing 12 physical activities [110]. The data were collected using Shimmer2 wearable sensors placed on the chest, wrist, and ankle. The sensor on the chest contains a 2-lead ECG. The activities range from simple ones like standing still or lying down to more complex ones like cycling or running. The publicly available dataset can be used to develop and evaluate activity recognition models. Wrist PPG During Exercise is a publicly available dataset of PPG signals collected during exercise. The dataset includes PPG signals from the wrist, electrocardiography (ECG) signals from the chest, and motion data from a 3-axis accelerometer and a 3-axis gyroscope [81]. The PPG signals were recorded during walking, running, and cycling at two different resistance levels.