Considerations for development of sensing and monitoring tools to facilitate treatment and care of persons with lower-limb loss: a review

Abstract

Sensing and monitoring technologies offer enormous potential to enhance the quality of healthcare provided to persons with lower-limb loss. Incorporation of these technologies into the rehabilitation process creates opportunities for a multidimensional exchange of timely, relevant, and meaningful health information between patients, their prostheses, and healthcare providers. Here, the authors envision a conceptual model for enhancing prosthetic rehabilitation through use of integrated physical and/or biological sensors and remote monitoring methods. Several specific applications that target treatment, diagnosis, and prognosis of health issues faced by persons with limb loss are proposed in an effort to demonstrate how collecting and using objective data can facilitate clinical decision making. Contemporary integrated sensors that may be used in these applications are reviewed and their limitations discussed. It is hoped that the considerations proposed here may serve to stimulate development of clinically useful monitoring and sensing technologies and promote their integration into routine amputation rehabilitation.

Keywords: ambulatory monitoring; amputee; diagnosis; limb loss; outcome assessment; prognosis; prosthesis; remote sensing technology; therapy/treatment; wireless sensors.

FIGURE 1

Traditional model of amputee rehabilitation where experience informs clinical decisions and is augmented by information obtained from a patient.

FIGURE 2

Evidence-based model of amputee care, where both scientific evidence and clinical experience are incorporated into the clinical decision-making process, augmented by information obtained from a patient.

FIGURE 3

Multidimensional model of amputee rehabilitation where information is obtained from and exchanged between a patient, a sensing device, and a practitioner. This model allows for practitioners to use objective information to make informed clinical decisions as well as potentially contribute data to a growing body of scientific evidence.

FIGURE 4

Example of daily loading feedback provided to a hypothetical patient who has received a first prosthesis. Data shows daily performance (i.e., vertical loading) with respect to established clinical targets. The patient can use the feedback to adjust loading patterns when he or she loads the prosthesis too much, or not enough.

FIGURE 5

Summary chart of hypothetical patient activity, highlighting time and intensity of loading. With this kind of information, the practitioner can visualize the patient’s progress and observe unexpected changes in patterns that may require intervention.

FIGURE 6

a,b,c. Static stability for a hypothetical patient, as indicated by relative loading of the prosthesis during 30 seconds of quite standing. The grey area represents a suggested zone of stability for static standing. Sensor data is expected to help differentiate between patients and conditions, for example (A) a healthy below-knee amputee, (B) a patient that presents with poor proprioception and balance when wearing a multi-axial foot, (C) a patient that presents with poor proprioception and balance after transition to a solid-ankle foot. The pattern in (C) shows that the patient is more stable wearing a solid-ankle foot. Data were collected using a piezoelectric force sensor positioned in the prosthesis.

FIGURE 7

Step activity and limb-socket slippage for a hypothetical patient. The practitioner-generated report shows that, after five days of use, slip occurs regularly. This suggests that a clinical intervention is necessary to avoid residual limb tissue breakdown or falls due to poor suspension.