Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Zampogna, Alessandro; Mileti, Ilaria; Palermo, Eduardo; Celletti, Claudia; Paoloni, Marco; Manoni, Alessandro; Mazzetta, Ivan; Costa, Gloria Dalla; Pérez-López, Carlos; Camerota, Filippo; Leocani, Letizia; Cabestany, Joan; Irrera, Fernanda; Suppa, Antonio

doi:10.3390/s20113247

Cited by 79 publications

(80 citation statements)

References 218 publications

(442 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This approach, also termed “sensor fusion”, provides more complete information on body motion, including data on muscle activity through sEMG wireless sensors [ 66 ]. Accordingly, innovative devices composed of both IMUs and sEMG are increasingly used to optimize the objective recognition of motor disorders in PD patients [ 67 , 68 ]. When using innovative wearable sensors, it is essential to consider also some limitations.…”

Section: Instrumental Assessment Of Nocturnal Movementsmentioning

confidence: 99%

“…Concerning IMUs, possible technical challenges include magnetic interferences, energy consumption, calibration loss, and errors of misalignment, orthogonality, and offset. Moreover, skin-electrode interface noise and crosstalk due to adjacent muscles may also affect recordings through sEMG sensors [ 68 ]. Figure 1 reports the main wearable technologies available for nocturnal movement assessment, including the actigraph and inertial and sEMG sensors.…”

Section: Instrumental Assessment Of Nocturnal Movementsmentioning

confidence: 99%

See 1 more Smart Citation

Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies

Zampogna

Manoni

Asci

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

In Parkinson’s disease (PD), abnormal movements consisting of hypokinetic and hyperkinetic manifestations commonly lead to nocturnal distress and sleep impairment, which significantly impact quality of life. In PD patients, these nocturnal disturbances can reflect disease-related complications (e.g., nocturnal akinesia), primary sleep disorders (e.g., rapid eye movement behaviour disorder), or both, thus requiring different therapeutic approaches. Wearable technologies based on actigraphy and innovative sensors have been proposed as feasible solutions to identify and monitor the various types of abnormal nocturnal movements in PD. This narrative review addresses the topic of abnormal nocturnal movements in PD and discusses how wearable technologies could help identify and assess these disturbances. We first examine the pathophysiology of abnormal nocturnal movements and the main clinical and instrumental tools for the evaluation of these disturbances in PD. We then report and discuss findings from previous studies assessing nocturnal movements in PD using actigraphy and innovative wearable sensors. Finally, we discuss clinical and technical prospects supporting the use of wearable technologies for the evaluation of nocturnal movements.

show abstract

Section: Instrumental Assessment Of Nocturnal Movementsmentioning

confidence: 99%

Section: Instrumental Assessment Of Nocturnal Movementsmentioning

confidence: 99%

Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies

Zampogna

Manoni

Asci

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yet, the most consistent predictors of falls are impairments gait and balance disorders [6,7]. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to gait and balance [8]. Therefore, the incidence of falls is high in neurological patients; compared with healthy subjects, neurological patients had a 49% increased risk of falling within 20 months [9,10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

Zhou

Rehman

Hansen

et al. 2020

Sensors

View full text Add to dashboard Cite

Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49–80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions.

show abstract

“…Int J Spine Res 2(1): 060-063. DOI: https://dx.doi.org/10.17352/ijsr.000016 due to impaired balance and can be useful in identifying what is happening [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…or closed-eyes conditions, VR applications can more fi nely differentiate the visual and vestibular systems, by providing a wide range of visual distortions. These systems also allow the balance to be evaluated in all planes, it allows therapists to better record the assessments of the individual's balance problem [8,18].…”

Section: Introductionmentioning

confidence: 99%

Technological methods used in evaluating the balance

Soyuer¹

2020

Int J Spine Res

View full text Add to dashboard Cite

Balance is evaluated within the concept of coordination and is defi ned as the ability to maintain the body's center of gravity on the support surface. Technological methods used in the assessment of balance are robotic systems, virtual reality applications, tele-rehabilitation, web-based applications and sensor applications. In this review, we aimed to explain today's technological assessment methods and their relationship with technological rehabilitation in balance assessment. The technological method to be used in balance assessment suitability of the patient, ease of use, time saving, active participation, depending on the person's fi nancial situation and the severity of the disease although the advantages vary, It seems that it may differ. More studies are needed for a more objective assessment of balance, designing products and using these technologies in different patient groups.

show abstract

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Cited by 79 publications

References 218 publications

Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies

Shedding Light on Nocturnal Movements in Parkinson’s Disease: Evidence from Wearable Technologies

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

Technological methods used in evaluating the balance

Contact Info

Product

Resources

About