Syncplay skipping to the end1/5/2024 ![]() The dataset published on MendeleyData 1 is organized as described in Fig. 1, and for each volunteer the dataset contains 7 comma-separated value (csv) files, i.e., one file for each of the 6 IMU sensors worn by the volunteer on different body parts, as described in Fig. 1, namely left lower arm (lla.csv), left upper arm (lua.csv), right lower arm (rla.csv), right upper arm (rua.csv) and right thigh (rt.csv). Each volunteer performed each activity at least 14 times, with the notable exception of the Walk activity that has been performed 40 times, in different sequences and alternating the hand wearing the IMUs. The dataset contains multiple instances of the 9 ADL-related actions presented in Table 1 and performed by 10 volunteers. In this article, we present a multi-sensory dataset concerning the execution of actions related to the Activities of Daily Living (ADL). The experiments have been recorded with an RGB camera used for data labelling.ĭepartment of Electronics and Electrical Engineering, Keio University, Hiyoshi, Kohokuku, Yokohama, Kanagawa, Japan (35.555659, 139.653391).ĭata identification number: 10.17632/wjpbtgdyzm.1 Each volunteer performed an overall 186 ADL-related instances of activity while wearing 6 IMU sensors (two on each arm, one on the back and one on the right thigh). The dataset has been collected with 10 healthy volunteers. The order in which the activities are performed has been chosen to ensure a high variety, and volunteers have been asked to perform the activities with both their hands independently of their dominant hand. The data collection process considered 9 ADL (related to walk, sit down, stand up, open a door, close a door, pour water, drink using a glass, teeth brushing and clean a table). Six 9-axis IMUs (TSND 151) RGB Camera (Logitech HD Pro Webcam C920). Human-Computer Interaction, Activity Recognition, Ambient Intelligence. The videos recorded during the experiments have been used only for labelling purposes, and they are not published. The dataset features an accurate data labelling done via manual annotation performed thanks to videos recorded by an RGB camera. The dataset contains data from six 9-axis Inertial Measurement Units (IMUs), worn by each volunteer (two for each arm, one on the back and one on the right thigh). The dataset acquisition involved 10 volunteers performing 186 ADL instances, for a grand total of over 1860 examples. The collection process adopted for building this dataset considers nine ADL-related activities, which have been performed in different locations and involving the usage of both left and right arms. ![]() Typical basic ADLs include walking, such postural transitions as getting up or sitting down, as well as behaviours related to feeding, such as drinking or eating with knife and fork, or personal hygiene, e.g., teeth brushing. These are the activities that contribute to an assessment of the overall status of elderly or people with special needs, possibly suffering from mild cognitive impairments. The article describes a multi-sensory dataset related to the Activities of Daily Living (ADL).
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