Reconstruction of body motion during self-reported losses of balance in community-dwelling older adults

Abstract

Older adults experience slips, trips, stumbles, and other losses of balance (LOBs). LOBs are more common than falls and are closely linked to falls and fall-injuries. Data about real-world LOBs is limited, particularly information quantifying the prevalence, frequency, and intrinsic and extrinsic circumstances in which they occur. This paper describes a new method to identify and analyze LOBs through long-term recording of community-dwelling older adults. The approach uses wearable inertial measurement units (IMUs) on the feet, trunk and one wrist, together with a voice recorder for immediate, time-stamped self-reporting of the type, context and description of LOBs. Following identification of an LOB in the voice recording, concurrent IMU data is used to estimate foot paths and body motions, and to create body animations to analyze the event. In this pilot study, three older adults performed a long-term monitoring study, with four weeks recording LOBs by voice and two concurrent weeks wearing IMUs. This report presents a series of LOB cases to illustrate the proposed method, and how it can contribute to interpretation of the causes and contexts of the LOBs. The context and timing information from the voice records was critical to the process of finding and analyzing LOB events within the voluminous sensor data record, and included much greater detail, specificity, and nuance than past diary or smartphone reporting.

Figure 1

Flowchart showing the key components of the proposed method. IMU data (both feet, waist, one wrist) are collected continuously during daily life. Voice recorders (other arm) are used to provide LOB context. The time-stamp from the voice recordings defining a time-window when the LOB occurred. The foot trajectories are combined with trunk and wrist pose information to create full body animations, which help identifying the exact location of the LOB. Finally, the LOB is analyzed for cause and kinematic response.

Figure 2

A loss of balance experienced by participant #1, with voice annotation, “Walking in the parking lot—lost my balance—regained my balance by touching the car.” (left) Data from IMUs. (right) Reconstructed body motions. The signals show a change in trunk pitch angle followed by a pattern of short steps used to recover. The cause of this LOB is not known, but a side-step and pause illustrates the recovery technique. See also the animation in supplementary material.

Figure 3.

Another loss of balance experienced by participant #1, with voice annotation: “Lost balance when I was in by the computer—regained balance without fully falling.” (left) Data from IMUs. (right) Reconstructed body motions. The right toe is caught on a stationary object, which interrupts its motion, forcing a quick step, followed by a recovery in mid-step on the left. The animation also shows immediate compensatory arm and trunk motion. See also the animation in supplementary material.

Figure 4.

Loss of balance experienced by participant #2, with voice annotation, “Activity for the last hour and half or so. Got to our cottage, about 2-3 feet snow in the driveway. Probably 100-150 foot walk from the car on the road to the house in numerous trips, probably a lot of slip and slide.” (left) Data from IMUs. (right) Reconstructed body motions. The event starts with a faulty step of the right foot, shown as an unusual slow, sideways step. It is followed by a quick compensatory left foot step. See also the animation in supplementary material.

Figure 5.

Loss of balance experienced by participant #3, with voice annotation, “Slipping and sliding on ice. Regained my balance.” (left) Data from IMUs. (right) Reconstructed body motions. Die left foot slip is followed by several short and quick steps. The large compensatory trunk and arm response suggests a relatively severe disturbance. See also the animation in supplementary material.