As wearable biometric sensors and applications evolve, the focus increasingly shines on “clinical grade” or “medical grade” data. As we’ve written before, two factors drive the demand for greater accuracy, validity, and reliability: Personal devices, and wearables used in clinical settings or prescribed by healthcare professionals. If data gathered and reported by consumer devices is only roughly accurate, physicians will ignore it. If medical staff employ wearable sensors working with patients, there’s too much at stake for imprecise data.
We’ve noted Valencell’s focus on sensor data accuracy in the past. Shimmer is another sensor company that develops and manufactures medical grade wearable sensors. Shimmer works with universities, research institutions, and companies around the globe with sensor components, accessories, and software. Shimmer also offers development services to work with customers from concept to commercialization. The company has end-to-end solutions for specific applications. One complete solution is QTUG, a tool for identifying older adults at risk of falling. This is a serious issue because falls are the leading cause of fatal and non-fatal injuries among the elderly. According to Shimmer, QTUG can report an objective assessment of gait, mobility, frailty, falls risk, and a detailed explanation of TUG or Timed Up and Go test results, a standard mobility measure. The system can accomplish all this in less than five minutes.
According to Shimmer, the current standard methods of assessing fall risk are based on subjective observation of gait and mobility. Shimmer’s QTUG — the “Q” stands for “Quantitative” — uses wireless sensors placed on each leg while the patient performs the exercise. QTUG not only provides an objective mobility assessment and falls risk, it also can indicate specific impairment by comparing the data with a reference population. QTUG has the CE mark as a Class I medical device in Europe and is FDA registered as a Class I (exempt) medical device. In addition to QTUG Shimmer also has end-to-end solutions for quantitative assessment of gait and another that uses validated GSR and ECG data to gain simultaneous insights into the unconscious reactions of large groups.