Discriminating Between Fallers and Non-Fallers Using Kinematic Data from the Heel2Toe™ Wearable Sensor.

The study sample comprised 387 participants, of whom 68 (17.6%) self-reported falling in the past year.

• Data came from experimental use of the Heel2Toe™ sensor in a variety of settings, including demonstrations and clinical research studies.
• Falls were self-reported as having occurred in the past year.
• The proportion of fallers (17.6%) reflects a community-relevant fall prevalence.

Ankle angular velocity (AV) at heel strike was identified as the key kinematic parameter to discriminate between fallers and non-fallers.

• Logistic regression with a natural cubic spline with 3 degrees of freedom was used to identify the discriminating variable.
• Among multiple AV metrics derived from the ankle during walking, the angle at heel strike was specifically identified as the discriminating measure.
• The ankle was prioritized as most relevant because ankle kinematics relate most closely to causes of falls, trips, slips, and mis-steps.

An algorithm to estimate fall risk was proposed using regression parameters from the logistic regression model.

• Applying the algorithm to the existing data yielded a range of probabilities from 0.0480 to 0.7245 depending on the age of the person assessed.
• Age was incorporated as a modifying factor in the algorithm, influencing the estimated probability of fall risk.
• The authors note that further testing of this algorithm in different samples is warranted.

Most falls occur while walking, making gait quality and kinematic parameters logical therapeutic targets for fall prevention.

• Many temporo-spatial variables have been implicated in increased fall risk, but these are dependent upon kinematic parameters of the joints involved in the gait cycle.
• The widespread availability of wearable sensors has made the acquisition of kinematic data feasible in a variety of settings.
• Ankle kinematics were considered most relevant as they relate most closely to causes of falls, trips, slips, and mis-steps.

The Heel2Toe™ wearable sensor was used to acquire ankle angular velocity metrics during walking across multiple settings.

• Data collection occurred in a variety of settings including demonstrations and clinical research studies.
• The sensor captured kinematic data related to ankle angular velocity during the gait cycle.
• The study design was comparative, examining AV metrics between people who had or had not experienced a fall in the past year.