Tracking and Characterization of Spinal Cord-Injured Patients by Means of RGB-D Sensors

Abstract

In physical rehabilitation, motion capture solutions are well-known but not as widespread as they could be. The main limit to their diffusion is not related to cost or usability but to the fact that the data generated when tracking a person must be elaborated according to the specific context and aim. This paper proposes a solution including customized motion capture and data elaboration with the aim of supporting medical personnel in the assessment of spinal cord-injured (SCI) patients using a wheelchair. The configuration of the full-body motion capturing system is based on an asymmetric 3 Microsoft Kinect v2 sensor layout that provides a path of up to 6 m, which is required to properly track the wheelchair. Data elaboration is focused on the automatic recognition of the pushing cycles and on plotting any kinematic parameter that may be interesting in the assessment. Five movements have been considered to evaluate the wheelchair propulsion: the humeral elevation, the horizontal abduction of the humerus, the humeral rotation, the elbow flexion and the trunk extension along the sagittal plane. More than 60 volunteers with a spinal cord injury were enrolled for testing the solution. To evaluate the reliability of the data computed with SCI APPlication (APP) for the pushing cycle analysis, the patients were subdivided in four groups according to the level of the spinal cord injury (i.e., high paraplegia, low paraplegia, C7 tetraplegia and C6 tetraplegia). For each group, the average value and the standard deviation were computed and a comparison with similar acquisitions performed with a high-end solution is shown. The measurements computed by the SCI-APP show a good reliability for analyzing the movements of SCI patients’ propulsion wheelchair.

A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation

“…Four other studies focused on children with autism spectrum disorder (ASD) ( n = 4) [ 52 – 55 ] while there are two studies focused on patients with schizophrenia ( n = 2) [ 56 , 57 ] and patients with dementia ( n = 2) [ 58 , 59 ] respectively. The rest of the studies were conducted on various other diseases: Fragile X syndrome (FXS) [ 60 ], chronic neck pain [ 61 ], breast cancer [ 9 ], spinal cord injury (SCI) [ 62 ], amyotrophic lateral sclerosis (ALS) [ 63 ], adhesive capsulitis of shoulder (AC) [ 64 ], dystrophinopathy [ 65 ] and neuromotor diseases [ 66 ]. There were also studies that had been conducted on wheelchair users (n = 2) [ 67 , 68 ], people awaiting total knee arthroplasty (TKR) [ 69 ], patients with gait disturbance [ 70 ], patients with neurodevelopment disorders (NDD) [ 71 ], patients with tremor [ 72 ], patients with Duchenne muscular dystrophy (DMD) [ 73 ], patients with cervical dystonia (CD) [ 74 ] and patients with a variety of diagnoses [ 75 ].…”

“…Two studies used the MMC system to measure range of motion (ROM), and they suggested MMC could be an alternative to the goniometer as a tool for ROM assessment [ 64 , 75 ]. Three studies used the MMC system as a tool to analyze the wheelchair propulsion skills of wheelchair users [ 62 , 67 , 68 ]. Ten studies correlated or compared the MMC measurements with clinical assessment scales [ 18 , 23 , 27 , 38 , 46 – 48 , 58 , 65 , 74 ].…”

“…2020 [ 22 ] PD Patients with idiopathic PD (39); healthy controls (30) Smartphone Bradykinesia assessed by finger tapping Correlation of machine learning models with clinical ratings of bradykinesia Clinical setting Computer measures correlated well with clinical ratings of bradykinesia “The research provides a new tool to quantify bradykinesia. It could potentially be used to support diagnosis and monitoring of PD” [ 22 ] Zefinetti et al 2020 [ 62 ] SCI patients using a wheelchair Patients with SCI (60) Kinect v2 Kinematic data, including humeral elevation, horizontal abduction of humerus, humeral rotation, elbow flexion, trunk flexion/extension of wheelchair propulsion Correlation between the movements and the patients’ assessment Laboratory The measurements computed by the proposed system showed a good reliability for analyzing the movements of SCI patients’ wheelchair propulsion “The proposed markerless solutions are useful for an adequate evaluation of wheelchair propulsion” [ 62 ] Abbas et al 2021 [ 57 ] Schizophrenia Patients with Schizophrenia (18); healthy controls (9) Smartphone Head movement Comparison of head movement measurements between patients and healthy controls, relationship of head movement to schizophrenia symptom severity Home setting/ Natural environment Rate of head movement in participants with schizophrenia and those without differed significantly; head movement was a significant predictor of schizophrenia diagnosis “Remote, smartphone- based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement” [ 57 ] Ardalan et al 2021 [ 71 ] Neurodevelopmental Disorders Children with 16p11.2 mutation (15); TD children (12) A single point-and-shoot camera Gait synchrony, balance parameters Comparison of gait synchrony and balance in children with 16p11.2 mutation and TD children ...…”

Smart Sensors for Healthcare and Medical Applications

“…Seven articles focused on patients' motor functions for potential application in rehabilitation or in the assessment of their functional impairment [18][19][20][21][22][23]. The first article proposes a novel contactless method, based on electrostatic field, for extracting gait features of hemiplegic patients [18], together with an improved version of the traditional detrended cross-correlation analysis to analyze the signal collected on 10 hemiparetic patients and 10 healthy volunteers.…”

“…The authors found that the proposed method can quantify the gait difference between the hemiparetic patients and the healthy controls. The second paper proposes a solution to support medical personnel in the assessment of patients with spinal cord injury [19]. The authors present a system based on 3 RGB-D cameras (Microsoft Kinect v2) to assess patients' movements using a wheelchair.…”