The pull test (PT) is a common practice to assess the postural instability of patients with Parkinson's disease. Postural instability is a serious issue for elderly and people with neurological disease, which can cause falls. The implementation of the PT consists in observing the user response after providing a tug to the patients' shoulders, in order to displace the center of gravity from its neutral position. The validity of the test can be compromised by a nonstandard backward tug provided to the patient. The solution proposed in this paper consists of a low-cost multisensor system allowing an instrumented estimation of the input solicitation. Moreover, the system provides supplementary information on the user postural stability, by means of a set of features extracted from the user stabilogram. A wide set of experiments have been performed to assess the system capability to provide a rough classification between stable and unstable behaviors. Results obtained demonstrate the validity of the approach proposed, with very low rates of false positive and false negative.

A Wearable Device to Support the Pull Test for Postural Instability Assessment in Parkinson's Disease

Ando, Bruno;Baglio, Salvatore;Marletta, Vincenzo;Pistorio, Antonio;Dibilio, Valeria;Mostile, Giovanni;Nicoletti, Alessandra;Zappia, Mario
2018-01-01

Abstract

The pull test (PT) is a common practice to assess the postural instability of patients with Parkinson's disease. Postural instability is a serious issue for elderly and people with neurological disease, which can cause falls. The implementation of the PT consists in observing the user response after providing a tug to the patients' shoulders, in order to displace the center of gravity from its neutral position. The validity of the test can be compromised by a nonstandard backward tug provided to the patient. The solution proposed in this paper consists of a low-cost multisensor system allowing an instrumented estimation of the input solicitation. Moreover, the system provides supplementary information on the user postural stability, by means of a set of features extracted from the user stabilogram. A wide set of experiments have been performed to assess the system capability to provide a rough classification between stable and unstable behaviors. Results obtained demonstrate the validity of the approach proposed, with very low rates of false positive and false negative.
2018
Biomedical monitoring; Classification algorithms; clinical diagnosis; Detectors; diseases; Instruments; Parkinson's disease; sensors; signal processing.; Stability analysis; Instrumentation; Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/317527
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