The article describes a multi-sensory dataset related to the Activities of Daily Living (ADL). 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. 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. 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. The dataset acquisition involved 10 volunteers performing 186 ADL instances, for a grand total of over 1860 examples. 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 features an accurate data labelling done via manual annotation performed thanks to videos recorded by an RGB camera. The videos recorded during the experiments have been used only for labelling purposes, and they are not published. (C) 2020 The Authors. Published by Elsevier Inc.
A multi-sensory dataset for the activities of daily living
Marco Ruzzon;Alessandro Carfi';Fulvio Mastrogiovanni;
2020-01-01
Abstract
The article describes a multi-sensory dataset related to the Activities of Daily Living (ADL). 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. 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. 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. The dataset acquisition involved 10 volunteers performing 186 ADL instances, for a grand total of over 1860 examples. 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 features an accurate data labelling done via manual annotation performed thanks to videos recorded by an RGB camera. The videos recorded during the experiments have been used only for labelling purposes, and they are not published. (C) 2020 The Authors. Published by Elsevier Inc.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
