Plantar foot temperature during sitting and standing in participants with diabetes and controls

Continuous temperature data collected from the plantar (sole) of the foot of 13 participants with diabetes and 12 healthy control participants during sitting and standing. Collected with our custom personalised temperature sensing insoles (see paper for description).

If using this data please cite: Beach, C.; Cooper, G.; Weightman, A.; Hodson-Tole, E.F.; Reeves, N.D.; Casson, A.J. Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables. Sensors 2021, 21, 1717. https://doi.org/10.3390/s21051717

The data presented here has been converted into °C and filtered using a low-pass filter with cut-off frequency of 0.02 Hz.

Temperature data is provided in csv format and the participant metadata is in xlsx format. In addition data is provided in Python serialized (pickle) format to allow easy importing to Python (see below for steps to do this). Use of the Anaconda Distribution is recommended.

In the metadata, Testing Date 1 refers to date where pressure mat data was collected to inform the personalised insole design. Testing Date 2 refers to the date where the temperature data uploaded here was collected.

To import this data into Python use the following commands:
import numpy as np
import pandas as pd
import pickle as pkl
with open('participants.pkl', 'rb') as f:
participants= pkl.load(f) # This line needs to be indented, however Mendeley is removing this formatting

'participants' is then a dictionary containing dictionaries for each participants. Within each of these dictionaries are four Pandas DataFrame's containing the temperature data for each foot, for sitting and standing.
For example, if you wanted to access the DataFrame for the left foot of participant H1 during standing you would type:
participants['H1']['sitting_L']

Within each DataFrame each column corresponds to:
Time: The timestamp of each datapoint in HH:MM:SS as recorded by the iPhone app
Ch0: Temperature data from the Hallux
Ch1: Temperature data from the 1st Metatarsal Head
Ch2: Temperature data from the 5th Metatarsal Head
Ch3: Temperature data from the Calcaneus

So if you wanted just the temperature data from the left 1st Metatarsal Head of participant H1 during sitting you would type:
participants['H1']['sitting_L']['Ch1']

本数据集采集了13名糖尿病受试者与12名健康对照受试者在坐位与站立状态下的足底（脚掌）连续温度数据。采集所用设备为我们定制的个性化温度传感鞋垫（详细说明参见相关论文）。

若使用本数据集，请引用以下文献：Beach, C.; Cooper, G.; Weightman, A.; Hodson-Tole, E.F.; Reeves, N.D.; Casson, A.J. 基于个性化3D打印可穿戴设备的糖尿病患者动态足底温度监测. Sensors 2021, 21, 1717. https://doi.org/10.3390/s21051717

本数据集已转换为摄氏度（°C）格式，并通过截止频率为0.02 Hz的低通滤波器完成滤波处理。

温度数据以CSV格式存储，受试者元数据存储于XLSX格式文件中。此外，数据集还提供Python序列化（pickle）格式文件，以便于在Python环境中快速导入（具体导入步骤详见下文）。推荐使用Anaconda发行版进行数据处理。

在元数据文件中，“测试日期1”指用于指导个性化鞋垫设计的压力垫数据采集日期；“测试日期2”指本数据集上传的温度数据的实际采集日期。

若需将数据导入Python环境，请使用下述代码：
import numpy as np
import pandas as pd
import pickle as pkl
with open('participants.pkl', 'rb') as f:
participants= pkl.load(f) # 此处需缩进，但Mendeley软件已移除该格式设置

上述代码中，`participants`为一个嵌套字典，包含每位受试者的子字典。每个子字典内包含四个Pandas数据框（Pandas DataFrame），分别对应双足在坐位与站立状态下的温度数据。

例如，若需获取受试者H1左侧足部在坐位时的温度数据框，可执行如下代码：
participants['H1']['sitting_L']

每个数据框的列含义如下：
- Time：每个数据点的时间戳，格式为HH:MM:SS，由iPhone应用记录
- Ch0：来自拇趾（Hallux）的温度数据
- Ch1：来自第一跖骨头的温度数据
- Ch2：来自第五跖骨头的温度数据
- Ch3：来自跟骨（Calcaneus）的温度数据

例如，若仅需获取受试者H1坐位时左侧足部第一跖骨头的温度数据，可执行如下代码：
participants['H1']['sitting_L']['Ch1']