Abstract.The upper limbs plays a key role in the performance of ADL, from support to total execution. However, Often rehabilitations therapies sacrifices the regaining of functionality to other functions as transportation. Therefore an automatic system capable of recognize ADL based on the information provided by upper limbs is needed. Lots of algorithms have been reported using inertial sensors with limited results. The aim of this work is to describe an algorithm to recognize some ADL performed with the upper limb, such as eating, drinking, talking by phone, combing hair and brushing teeth. The algorithm is based on an alternative novel sensor that provides information of the vertical displacement of the wrist relative to the shoulder, and can be used in a free-living environment. The detection system combines decision trees (DT) and Hidden Markov Models (HMM). Efficiencies reported goes from 61% up to 100%.
We need studies of better methodological quality to establish the Parkinson's disease stage in which auditory cues are most beneficial, as well as to determine the most effective type and frequency of the auditory cue during gait initiation and turning in patients with Parkinson's disease.
Abstract-Feeding and drinking are Activities of Daily Living which can be used to assess the motor control and functional ability of the upper limb. This paper presents the upperlimb kinematics during the execution of feeding and drinking activities, such analysis consisted in the measurement of angles of flexion for trunk and arm. Eight healthy subjects performed these activities in a simulated-environment while they were video recorded. Markers on anatomical landmarks were used to analyze the kinematics of the upper limb in the sagittal plane. Additionally an electro-hydraulic sensor was attached to each upper limb to assess the vertical position of the wrist relative to the shoulder. Results showed a difference on the angles of the elbow and trunk. The electro-hydraulic sensor showed to be an efficient way to record the vertical position of wrist.
Activities of Daily Living (ADL) have a background of selfsufficiency and survival function. Upper limbs participate actively in many ADL; particularly, activities related to feeding, communication, and grooming. The performance of such activities is a parameter of independence. Various researchers have studied ADL in a free-living environment by using inertial sensors. However, functional-activity recognition with low recognition rate is a persistent result. This work proposes the use of well-known clustering techniques for ADL recognition by using as feeding signal the vertical trajectory of wrist relative to the shoulder.
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