Multimodal Interaction Strategies for Walker-Assisted Gait: A Case Study for Rehabilitation in Post-Stroke Patients

• DOI: 10.1007/s10846-023-02031-w
• Publication Date: 2024-01-16T10:02:32Z
• Authors: Mario F. Jimenez; Ricardo C. Mello; Flavia Loterio; Anselmo Frizera-Neto
• Keywords: 0209 industrial biotechnology; Artificial intelligence; Cognitive Neuroscience; Population; FOS: Mechanical engineering; 02 engineering and technology; Haptic technology; Leverage (statistics); Multimodal interaction; Engineering; Health Sciences; Eye Tracking in Human-Computer Interaction; Gait; Human–computer interaction; Rehabilitation; Life Sciences; Principles and Interventions in Stroke Rehabilitation; Computer science; Tactile Perception and Cross-modal Plasticity; Mechanical engineering; Human-Computer Interaction; Environmental health; Physical medicine and rehabilitation; Computer Science; Physical Sciences; Medicine; Stroke (engine); Sensory Substitution; Physical therapy; Neuroscience

AbstractStroke has been considered the main cause of neuromuscular damages worldwide and one of the most common causes of walking disabilities, with approximately 60% of the individuals suffering from persistent problems in walking. These patients generally use technical aids for walking to achieve independent gait, however, when cognitive impairments are also present, conventional assistive devices such as walkers could be difficult to handle. By leveraging multimodal interfaces, smart walkers can offer natural and intuitive human-robot interaction. In this work, we present two multimodal interaction strategies for smart walkers focusing on guiding post-stroke patients through their environment. These strategies leverage different communication channels and provide distinct levels of guidance: one strategy uses haptic feedback and a visual interface to indicate the desired path to the user, while the other strategy uses haptic feedback and a virtual torque to maintain the user on path. We also present two case studies with post-stroke patients to preliminarily validate these interaction strategies with their target population and to collect valuable insight as to how multimodal strategies for smart walkers can be enhanced to deal with the characteristic asymmetries of post-stroke patients. Our results show that both strategies can guide the volunteers, however, the first one demands more effort from the volunteer and is more suited for patients with increased levels of independence. The second interaction strategy allows for higher linear velocity (Volunteer 1, $$\varvec{0.18}$$

0.18

$$\varvec{\pm 0.026}$$

± 0.026

$$\varvec{m/s}$$

m / s

; Volunteer 2, $$\varvec{0.22}$$

0.22

$$\varvec{\pm 0.0283}$$

± 0.0283

$$\varvec{m/s}$$

m / s

) than the first one (Volunteer 1, $$\varvec{0.10}$$

0.10

$$\varvec{\pm 0.031}$$

± 0.031

$$\varvec{m/s}$$

m / s

; Volunteer 2, $$\varvec{0.20}$$

0.20

$$\varvec{\pm 0.012}$$

± 0.012

$$\varvec{m/s}$$

m / s

), suggesting improved guidance.