[ARTICLE] Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system – Full Text

Abstract

Background

The effect of rehabilitative training after stroke is dose-dependent. Out-patient rehabilitation training is often limited by transport logistics, financial resources and a lack of motivation/compliance. We studied the feasibility of an unsupervised arm therapy for self-directed rehabilitation therapy in patients’ homes.

Methods

An open-label, single group study involving eleven patients with hemiparesis due to stroke (27 ± 31.5 months post-stroke) was conducted. The patients trained with an inertial measurement unit (IMU)-based virtual reality system (ArmeoSenso) in their homes for six weeks. The self-selected dose of training with ArmeoSenso was the principal outcome measure whereas the Fugl-Meyer Assessment of the upper extremity (FMA-UE), the Wolf Motor Function Test (WMFT) and IMU-derived kinematic metrics were used to assess arm function, training intensity and trunk movement. Repeated measures one-way ANOVAs were used to assess differences in training duration and clinical scores over time.

Results

All subjects were able to use the system independently in their homes and no safety issues were reported. Patients trained on 26.5 ± 11.5 days out of 42 days for a duration of 137 ± 120 min per week. The weekly training duration did not change over the course of six weeks (p = 0.146). The arm function of these patients improved significantly by 4.1 points (p = 0.003) in the FMA-UE. Changes in the WMFT were not significant (p = 0.552). ArmeoSenso based metrics showed an improvement in arm function, a high number of reaching movements (387 per session), and minimal compensatory movements of the trunk while training.

Conclusions

Self-directed home therapy with an IMU-based home therapy system is safe and can provide a high dose of rehabilitative therapy. The assessments integrated into the system allow daily therapy monitoring, difficulty adaptation and detection of maladaptive motor patterns such as trunk movements during reaching.

 

Background

Functional outcome following stroke is positively correlated with the dose of the applied rehabilitative intervention [1]. Therefore, post-stroke therapy should be provided at a high intensity, a high frequency and over long periods of time [1, 2]. However, the delivery of intensive physical therapy requires extensive therapist support, is expensive, and is often limited by the low compliance and lack of motivation to perform rehabilitative training at the recommended frequency [3]. This can lead to functional deterioration, e.g., by learned non-use of the affected limb [4].

Self-directed home therapy, supported by dedicated instrumented devices [5, 6, 7] or virtual reality gaming platforms [8, 9, 10, 11, 12, 13], could help to increase the dose of rehabilitation at low cost without the need for direct supervision by a therapist. It is important that such home therapy adapts to changes in the subject’s performance in order for it to remain challenging and motivating [8]. On the other hand, unsupervised rehabilitative training could lead to inefficient or harmful (i.e. maladaptive) movement sequences or pain, and could potentially worsen performance [8, 11, 14]. Home therapy should, therefore, include monitoring of movement quantity and quality. Several platforms dedicated to upper-extremity home rehabilitation have been proposed [6, 7, 15, 16, 17]. However, to the best of our knowledge only few were actually installed in the patients’ homes for several weeks and tested for feasibility beyond case studies. These home studies always involved some external supervision, in the form of e.g. on-site visits [16, 17], tele-monitoring and adaption [16, 17] or telephone calls [6, 7], which might have affected compliance and motivation and thereby therapy dosage. However, such an approach requires manpower, which limits the affordability and scalability of home-based therapy. The feasibility and compliance of completely unsupervised upper-limb stroke therapy over the course of several weeks remains to be investigated.

In this paper we investigate the feasibility of self-directed home training with the custom-designed ArmeoSenso system [18], a virtual reality arm rehabilitation platform based on wearable inertial measurement units (IMU). In a clinical study involving eleven patients with hemiparesis of the arm due to stroke, we evaluated the ability to deliver therapy at a high dose through simple-to-use and entertaining, yet functionally relevant and adaptive rehabilitation games. Unsupervised, automated assessments integrated into each therapy session allowed monitoring of arm function, and detection of undesired compensatory movements.

Methods

ArmeoSenso training system

ArmeoSenso comprises a motion capture system based on wearable sensors in combination with an all-in-one touch screen computer (Inspiron 2330, Dell Inc., Fig. 1a). The therapy software provides a user-friendly graphical user interface, two therapy games, and two short automated assessments of arm function [18]. For real-time tracking of arm and trunk movements, the patient wears three IMUs (MotionPod 3, Movea Inc.) fixed to the lower and upper arm as well as the trunk (Fig. 1a). The IMUs measure acceleration, angular velocity and the magnetic field, all in three dimensions, and stream this data wirelessly to a receiver block, which is connected to the computer via USB and serves as a docking station to charge the sensors. A kinematic reconstruction estimates the orientation of the trunk, the upper- and the lower arm based on the Madgwick algorithm [19] and the corresponding joint positions are computed with forward kinematics [20]. This reconstruction serves as input for the assessments and therapeutic virtual reality games (Fig. 1b). By using the same virtual kinematic parameters for each patient, virtual sizes, e.g. distances or the size of targets, are normalized to the patient’s body size. To discourage trunk inclination or rotation during pointing movements, the arm movements are computed and displayed relative to the trunk.

Fig. 1 System Overview and Study Outline. a: Photograph of a healthy subject using ArmeoSenso. b: Screenshot of the pointing task assessment: the virtual upper- and lower arm and the trunk are displayed. The arm points to a target. c: Sequence of a training session. Before each training session, two automated assessments are performed. d: Study outline: The ArmeoSenso system is installed in the patient’s home for six weeks. The patients are assessed clinically before the start, after three weeks, and after six weeks of training. Abbreviations: WMFT: Wolf Motor Function Test; FMA-UE: Fugl-Meyer Assessment Upper Extremity; NIHSS: National Institute of Health Stroke Scale. *system installation and patient instruction by a therapist

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