Recognition of low amplitude body vibrations via inertial sensors for wearable computing

Abstract

Pathological shaking of the body or extremities is widely known and might occur at chronic diseases e.g. Parkinson. The rhythmical shaking, also known as tremor, can be such intense that extremities are flapping. Under certain circumstances, healthy people also show a shivering and shaking of their body. For example, humans start to shiver whenever it is too cold or if feelings such as stress or fear become dominant. Some wearable devices that are in direct contact to the body, such as smartwatches or smartglasses, provide a sensing functionality of acceleration force that is sufficient to detect the tremor of the wearer. The tremor varies in frequency and intensity and can be identified, by applying detection algorithms and signal filtering. Former works figured that all endotherms show muscle vibrations. These vibrations occur in the condition of sleeping as well as when being awake, or in unconsciousness. Furthermore, the vibrations are also present when subjects are physically active, emotionally stressed, or absolutely relaxed. The vibration itself varies in structure, amplitude, and frequency. This paper shows that these muscle vibrations are measureable by acceleration sensors attached to the user, and provides an outlook to new applications in the future. It also proves that custom mobile devices are able to detect body and muscle vibration and should motivate designers to develop new applications and treatment opportunities.