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利用车载传感器估算使用手杖者的承重情况。

Weight-Bearing Estimation for Cane Users by Using Onboard Sensors.

机构信息

Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden.

Department of Electronic Technology, University of Malaga, 29071 Malaga, Spain.

出版信息

Sensors (Basel). 2019 Jan 26;19(3):509. doi: 10.3390/s19030509.

Abstract

Mobility is a fundamental requirement for a healthy, active lifestyle. Gait analysis is widely acknowledged as a clinically useful tool for identifying problems with mobility, as identifying abnormalities within the gait profile is essential to correct them via training, drugs, or surgical intervention. However, continuous gait analysis is difficult to achieve due to technical limitations, namely the need for specific hardware and constraints on time and test environment to acquire reliable data. Wearables may provide a solution if users carry them most of the time they are walking. We propose to add sensors to walking canes to assess user's mobility. Canes are frequently used by people who cannot completely support their own weight due to pain or balance issues. Furthermore, in absence of neurological disorders, the load on the cane is correlated with the user condition. Sensorized canes already exist, but often rely on expensive sensors and major device modifications are required. Thus, the number of potential users is severely limited. In this work, we propose an affordable module for load monitoring so that it can be widely used as a screening tool. The main advantages of our module are: (i) it can be deployed in any standard cane with minimal changes that do not affect ergonomics; (ii) it can be used every day, anywhere for long-term monitoring. We have validated our prototype with 10 different elderly volunteers that required a cane to walk, either for balance or partial weight bearing. Volunteers were asked to complete a 10 m test and, then, to move freely for an extra minute. The load peaks on the cane, corresponding to maximum support instants during the gait cycle, were measured while they moved. For validation, we calculated their gait speed using a chronometer during the 10 m test, as it is reportedly related to their condition. The correlation between speed (condition) and load results proves that our module provides meaningful information for screening. In conclusion, our module monitors support in a continuous, unsupervised, nonintrusive way during users' daily routines, plus only mechanical adjustment (cane height) is needed to change from one user to another.

摘要

活动能力是健康、积极生活方式的基本要求。步态分析被广泛认为是一种临床有用的工具,可用于识别活动能力的问题,因为识别步态特征中的异常对于通过训练、药物或手术干预来纠正这些异常是至关重要的。然而,由于技术限制,连续的步态分析是困难的,特别是需要特定的硬件,并且需要时间和测试环境来获取可靠的数据。如果用户在大部分行走时间内都携带可穿戴设备,那么可穿戴设备可能是一种解决方案。我们建议在拐杖上添加传感器以评估用户的活动能力。由于疼痛或平衡问题而无法完全支撑自己体重的人经常使用拐杖。此外,在没有神经障碍的情况下,拐杖上的负载与用户的状况相关。已经存在带传感器的拐杖,但通常依赖于昂贵的传感器,并且需要对设备进行重大修改。因此,潜在用户的数量受到严重限制。在这项工作中,我们提出了一种经济实惠的负载监测模块,以便可以将其广泛用作筛查工具。我们的模块的主要优点是:(i) 它可以部署在任何标准的拐杖上,只需进行最小的更改即可,不会影响人体工程学;(ii) 它可以每天在任何地方用于长期监测。我们已经使用 10 名不同的老年志愿者对我们的原型进行了验证,他们需要拐杖来行走,或者是为了平衡,或者是为了部分负重。志愿者被要求完成 10 米测试,然后自由移动一分钟。当他们移动时,测量拐杖上的负载峰值,对应于步态周期中最大支撑瞬间。为了验证,我们在 10 米测试期间使用计时器计算他们的步态速度,因为据报道这与他们的状况有关。速度(状况)和负载结果之间的相关性证明了我们的模块为筛查提供了有意义的信息。总之,我们的模块以连续、无人监督、非侵入的方式在用户的日常生活中监测支撑,并且只需要进行机械调整(拐杖高度)即可将其从一个用户切换到另一个用户。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/071b/6387159/6f2237af040a/sensors-19-00509-g001.jpg

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