ABSTRACT
Many amputees suffer from irritation and wounds as a result of poor residual limb volume management. Reasons contributing to failure to maintain volume properly include peripheral neuropathy, cognitive impairment, and social/cultural issues. Amputees commonly use socks of various thicknesses to account for diurnal limb volume loss. However, data relating to sock compliance is lacking due to an absence of a reliable way to collect usage data. A device was fabricated utilizing wireless RFID and socket-limb interface force detection technology to track sock usage and activity of an amputee. Pilot data was collected through both in-lab and out-of-lab protocols. The collected data showed encouraging results tracking interface force data, however accurate sock data collection was difficult. Suggested solutions include designing a more effective antenna and using the interface force data to detect limb presence to start a tag accumulator algorithm. Clinical applications for the Sock Monitor include intervention through alerting the amputee of a need for a sock change before tissue damage occurs and evidence for prosthetists to justify insurance reimbursement for components and socket replacements. The next step is to use a new prototype with better hardware and firmware to collect real-world usage data from a large group of amputees. A predictive model will be made and implemented to determine if intervention in sock usage improves comfort and limb tissue health.
ABSTRACT
Knowledge of how persons with amputation use their prostheses and how this use changes over time may facilitate effective rehabilitation practices and enhance understanding of prosthesis functionality. Perpetual monitoring and classification of prosthesis use may also increase the health and quality of life for prosthetic users. Existing monitoring and classification systems are often limited in that they require the subject to manipulate the sensor (e.g., attach, remove, or reset a sensor), record data over relatively short time periods, and/or classify a limited number of activities and body postures of interest. In this study, a commercially available three-axis accelerometer (ActiLife ActiGraph GT3X+) was used to characterize the activities and body postures of individuals with transtibial amputation. Accelerometers were mounted on prosthetic pylons of 10 persons with transtibial amputation as they performed a preset routine of actions. Accelerometer data was postprocessed using a binary decision tree to identify when the prosthesis was being worn and to classify periods of use as movement (i.e., leg motion such as walking or stair climbing), standing (i.e., standing upright with limited leg motion), or sitting (i.e., seated with limited leg motion). Classifications were compared to visual observation by study researchers. The classifier achieved a mean +/- standard deviation accuracy of 96.6% +/- 3.0%.
