Feasibility of a Sensor-Based Technological Platform in Assessing Gait and Sleep of In-Hospital Stroke and Incomplete Spinal Cord Injury (iSCI) Patients

Hendriks, Maartje M S; Hulst, Marije Vos-van der; Keijsers, Noël L.W.

doi:10.3390/s20102748

Cited by 4 publications

(7 citation statements)

References 57 publications

(87 reference statements)

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“…Therefore, we are confident that all gait bouts and their corresponding number of steps were correctly detected. Previously, we have shown that a sensor-based technological platform comprising IMUs can be used continuously and in an automated way in a clinical rehabilitation setting [ 28 ]. Taken together, we conclude that gait performance can be measured objectively, continuously, and automatically by using sensor technology during inpatient rehabilitation.…”

Section: Discussionmentioning

confidence: 99%

“…Important to note is that for clinical use, the gait algorithms have to be accurate enough to detect the minimal clinically important difference (MICD), which is between 0.1–0.2 m/s for gait speed in various rehabilitation patient groups [ 47 ]. Although the gait algorithm was responsible for most outliers, some outliers were caused by swapping the left and right sensors by the participant, which we considered a human interaction error [ 28 ]. For the successful implementation of sensor technology in the clinical setting, it is important to improve the gait algorithm for gait speeds below 0.5 m/s and to reduce the number of human interaction errors to a minimum.…”

Section: Discussionmentioning

confidence: 99%

“…Raw tri-axial accelerometer, gyroscope and magnetometer data were obtained using Shimmer ® 3 IMUs (Shimmer Sensing, Dublin, Ireland; 51 × 34 × 14 mm, 23.6 g) with a sampling frequency of 102.4 Hz and stored on a Microsoft ® Azure Platform. For a detailed description of the technological platform used, see Hendriks, et al [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

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Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Hendriks

Hulst

Weijs

et al. 2022

Sensors

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The aim of this study was to objectively assess and compare gait capacity and gait performance in rehabilitation inpatients with stroke or incomplete spinal cord injury (iSCI) using inertial measurement units (IMUs). We investigated how gait capacity (what someone can do) is related to gait performance (what someone does). Twenty-two inpatients (11 strokes, 11 iSCI) wore ankle positioned IMUs during the daytime to assess gait. Participants completed two circuits to assess gait capacity. These were videotaped to certify the validity of the IMU algorithm. Regression analyses were used to investigate if gait capacity was associated with gait performance (i.e., walking activity and spontaneous gait characteristics beyond therapy time). The ankle positioned IMUs validly assessed the number of steps, walking time, gait speed, and stride length (r ≥ 0.81). The walking activity was strongly (r ≥ 0.76) related to capacity-based gait speed. Maximum spontaneous gait speed and stride length were similar to gait capacity. However, the average spontaneous gait speed was half the capacity-based gait speed. Gait capacity can validly be assessed using IMUs and is strongly related to gait performance in rehabilitation inpatients with neurological disorders. Measuring gait performance with IMUs provides valuable additional information about walking activity and spontaneous gait characteristics to inform about functional recovery.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Hendriks

Hulst

Weijs

et al. 2022

Sensors

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“…This observational cohort study was carried out at the Sint Maartenskliniek (Ubbergen, the Netherlands). This study was part of an overarching study [ 39 ], which aimed to develop a sensor-based technological platform to monitor gait and sleep. It was performed in the clinical treatment environment of the rehabilitation department to integrate the intervention in a realistic setting.…”

Section: Methodsmentioning

confidence: 99%

Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study

Hendriks

Lotringen

Hulst

et al. 2021

JMIR Mhealth Uhealth

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Background Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=−2.1, P=.04) and movement activity (t18=−1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. Conclusions It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants.

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confidence: 99%

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

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Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

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Feasibility of a Sensor-Based Technological Platform in Assessing Gait and Sleep of In-Hospital Stroke and Incomplete Spinal Cord Injury (iSCI) Patients

Cited by 4 publications

References 57 publications

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study

Multimodal Sensing in Stroke Motor Rehabilitation

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