TWM463402U - Falling down detection system with automatic tracking - Google Patents
Falling down detection system with automatic tracking Download PDFInfo
- Publication number
- TWM463402U TWM463402U TW102211175U TW102211175U TWM463402U TW M463402 U TWM463402 U TW M463402U TW 102211175 U TW102211175 U TW 102211175U TW 102211175 U TW102211175 U TW 102211175U TW M463402 U TWM463402 U TW M463402U
- Authority
- TW
- Taiwan
- Prior art keywords
- depth
- subject
- processing unit
- skeleton
- images
- Prior art date
Links
Landscapes
- Emergency Alarm Devices (AREA)
- Alarm Systems (AREA)
Description
本新型是有關於一種人體動作偵測系統,特別是指一種具自動追蹤之跌倒偵測系統。The present invention relates to a human motion detection system, and more particularly to a fall detection system with automatic tracking.
台灣在民國八十二年時邁入高齡化社會,且高齡化伴隨而來的是老年人照護的問題,而行政院衛生署統計資料顯示,台灣六十五歲以上老人的死亡原因,事故傷害排名第七,事故傷害中又以跌倒為第二大死因,由此可見跌倒對老人來說是致命的傷害,老人跌倒常導致身體功能受損與日常生活活動能力喪失,若能提早發現跌倒事件並即時處理,就可以降低跌倒所造成的傷害。Taiwan entered an aging society in the 82nd year of the Republic of China, and the aging is accompanied by the problem of elderly care. The statistics of the Health Department of the Executive Yuan show that the cause of death of the elderly over 65 years old in Taiwan is injured. Ranked seventh, the fall is the second leading cause of death in accident injuries. It can be seen that the fall is a fatal injury to the elderly. The fall of the elderly often leads to impaired physical function and loss of activities of daily living. If the fall event is detected early And deal with it immediately, you can reduce the damage caused by the fall.
習知一種使用加速度計和陀螺儀之跌倒偵測系統是利用Arduino開發板,搭配配戴於身上的三軸加速度計與雙軸陀螺儀來判斷使用者是否跌倒。由於上述的跌倒偵測系統是需要在受偵測者的身上配戴感測器,對於老人家來說是一種負擔,除了有穿脫的困擾外,還容易弄丟感測器或是忘記配戴感測器的問題。A fall detection system using an accelerometer and a gyroscope is an Arduino development board that is equipped with a three-axis accelerometer and a dual-axis gyroscope to determine whether the user has fallen. Since the above-mentioned fall detection system needs to wear a sensor on the subject of the subject, it is a burden for the elderly, and in addition to the trouble of wearing and taking off, it is easy to lose the sensor or forget to wear it. Sensor problem.
因此,本新型之目的,即在提供一種具自動追蹤之跌倒偵測系統。Therefore, the purpose of the present invention is to provide a fall detection system with automatic tracking.
於是本新型具自動追蹤之跌倒偵測系統包含一旋轉載台、一深度感測單元,及一處理單元。Therefore, the novel fall detection system with automatic tracking includes a rotating stage, a depth sensing unit, and a processing unit.
該深度感測單元設置於該旋轉載台,且可對於一存在有至少一受測者的空間進行感測而取得多數深度圖像。The depth sensing unit is disposed on the rotating stage, and can obtain a majority depth image for sensing a space in which at least one subject exists.
該處理單元電連接該旋轉載台及該深度感測單元,且內建一判斷程式。The processing unit is electrically connected to the rotating stage and the depth sensing unit, and has a built-in judgment program.
其中,該處理單元辨別出該等深度圖像中該受測者的位置,且將各深度圖像中該受測者的位置依時間順序組合產生一移動路徑,該處理單元依據該移動路徑進行對於該旋轉載台的旋轉控制,以水平地帶動旋轉該深度感測單元使得該深度感測單元的最佳感測範圍一直是涵蓋該受測者。The processing unit identifies the position of the subject in the depth image, and combines the positions of the subject in each depth image in time series to generate a moving path, and the processing unit performs the moving path according to the moving path. For the rotation control of the rotary stage, the depth sensing unit is rotated horizontally so that the optimal sensing range of the depth sensing unit always covers the subject.
其中,該處理單元對於該等深度圖像中的該受測者進行骨架辨識處理,以得到多數分別對應於該等深度圖像的骨架圖像,該處理單元時序地處理該等骨架圖像得到各骨架圖像中的骨架的各節點的位移改變量,同時將各節點的位移改變量經由該判斷程式進行該受測者的跌倒動作辨識。The processing unit performs skeleton recognition processing on the subject in the depth images to obtain a plurality of skeleton images respectively corresponding to the depth images, and the processing unit sequentially processes the skeleton images to obtain The displacement change amount of each node of the skeleton in each skeleton image, and the displacement change amount of each node is used to identify the fall motion of the subject via the determination program.
較佳地,該深度感測單元包括一紅外線發射器,及一紅外線攝影機。該紅外線發射器對於該空間發射紅外線,該紅外線攝影機接收該空間內的物體所反射的紅外線而取得該等深度圖像。Preferably, the depth sensing unit comprises an infrared emitter and an infrared camera. The infrared emitter emits infrared rays to the space, and the infrared camera receives the infrared rays reflected by the objects in the space to obtain the depth images.
較佳地,該具自動追蹤之跌倒偵測系統還包含 一電連接該處理單元的通報單元,當該處理單元辨識出該受測者已跌倒時,控制該通報單元向外發出該跌倒信息。Preferably, the automatic tracking fall detection system further comprises A notification unit electrically connected to the processing unit, when the processing unit recognizes that the subject has fallen, controlling the notification unit to issue the fall information.
本新型的功效在於藉由該深度感測單元取得多數的深度圖像且經由該處理單元的影像處理後,進行該受測者的自動追蹤控制及該受測者的跌倒動作辨識,以達到可自動追蹤該受測者及跌倒動作偵測的功效。The utility model has the advantages that the depth image is obtained by the depth sensing unit, and after the image processing of the processing unit, the automatic tracking control of the subject and the fall of the subject are recognized. Automatically track the effect of the subject and fall detection.
1‧‧‧旋轉載台1‧‧‧Rotary stage
2‧‧‧深度感測單元2‧‧‧Deep sensing unit
21‧‧‧紅外線發射器21‧‧‧Infrared emitter
22‧‧‧紅外線攝影機22‧‧‧Infrared camera
3‧‧‧處理單元3‧‧‧Processing unit
31‧‧‧判斷程式31‧‧‧Judgement program
4‧‧‧通報單元4‧‧‧Notification unit
本新型之其他的特徵及功效,將於參照圖式的較佳實施例詳細說明中清楚地呈現,其中:圖1是一示意圖,說明本新型具自動追蹤之跌倒偵測系統的一較佳實施例的一深度感測單元及一旋轉載台的設置關係,且對於一存在有一受測者的空間進行深度感測;圖2是該較佳實施例的各元件的電連接關係;圖3是具有一深度圖像及一骨架圖像的示意圖;及圖4為一圖表,說明該受測者分別進行彎腰、蹲下及跌倒時,其骨架的頭部節點的所產生三種分別對應彎腰、蹲下及跌倒動作的位移變化量。Other features and effects of the present invention will be apparent from the following detailed description of the preferred embodiments, wherein: FIG. 1 is a schematic diagram illustrating a preferred embodiment of the novel automatic tracking tracking detection system. For example, a depth sensing unit and a rotating stage are disposed, and depth sensing is performed on a space in which a subject exists; FIG. 2 is an electrical connection relationship of each component of the preferred embodiment; A schematic diagram of a depth image and a skeleton image; and FIG. 4 is a diagram illustrating that the subject has three corresponding bending positions when the subject is bent, squatted, and fallen, respectively. The amount of displacement change in the underarm and fall movements.
參閱圖1及圖2,本新型具自動追蹤之跌倒偵測系統的一較佳實施例,包含一旋轉載台1、一深度感測單元2、一處理單元3,及一通報單元4。在本較佳實施例是採用微軟設計開發的kinect系統,該kinect系統的硬體及軟體分別為該深度感測單元2及該處理單元3的一部份。Referring to FIG. 1 and FIG. 2, a preferred embodiment of the automatic tracking and falling detection system of the present invention comprises a rotating stage 1, a depth sensing unit 2, a processing unit 3, and a notification unit 4. In the preferred embodiment, the kinect system designed and developed by Microsoft is used. The hardware and software of the kinect system are the depth sensing unit 2 and a part of the processing unit 3, respectively.
該深度感測單元2設置於該旋轉載台1,且對於 一存在有一受測者8的空間9持續進行感測而取得多數深度圖像(各深度圖像如圖3的左邊圖像所示)。該深度感測單元2包括一紅外線發射器21,及一紅外線攝影機22。該紅外線發射器21對於該空間9發射紅外線,該紅外線攝影機22接收該空間9內的物體所反射的紅外線而取得該等深度圖像。The depth sensing unit 2 is disposed on the rotating stage 1 and A space 9 in which a subject 8 exists continues to perform sensing to obtain a plurality of depth images (each depth image is as shown in the left image of FIG. 3). The depth sensing unit 2 includes an infrared emitter 21 and an infrared camera 22. The infrared ray emitter 21 emits infrared rays to the space 9, and the infrared ray camera 22 receives the infrared ray reflected by the object in the space 9 to obtain the depth images.
該處理單元3電連接該旋轉載台1及該深度感測單元2,且內建一判斷程式31。該處理單元3同時進行自動追蹤及跌倒偵測的運算處理。The processing unit 3 is electrically connected to the rotating stage 1 and the depth sensing unit 2, and a determination program 31 is built in. The processing unit 3 simultaneously performs arithmetic processing of automatic tracking and fall detection.
在自動追蹤的運算處理方面,該處理單元3根據各深度圖像的深度值資訊尋找該深度圖像中可能是受測者8的移動物體,並經由可辨別人體部位的該判斷程式31判斷該移動物體是否該受測者8,藉此辨別出該受測者8的位置。該處理單元3進一步將各深度圖像中該受測者8的位置依時間順序組合產生一移動路徑。該處理單元3依據該移動路徑進行對於該旋轉載台1的旋轉控制,以水平地帶動旋轉該深度感測單元2使得該深度感測單元2的最佳感測範圍一直是涵蓋該受測者8,以達到自動追蹤該受測者8的功能。In the processing of the automatic tracking, the processing unit 3 searches for the moving object that may be the subject 8 in the depth image according to the depth value information of each depth image, and determines the determined program 31 through the discriminable body part. Whether the moving object is the subject 8, thereby discriminating the position of the subject 8. The processing unit 3 further combines the positions of the subjects 8 in each depth image in time series to generate a moving path. The processing unit 3 performs rotation control on the rotating stage 1 according to the moving path, and rotates the depth sensing unit 2 horizontally to make the optimal sensing range of the depth sensing unit 2 always cover the subject. 8, to achieve automatic tracking of the function of the subject 8.
在跌倒偵測的運算處理方面,該處理單元3對於該等深度圖像中的該受測者8進行骨架辨識處理,以得到多數分別對應於該等深度圖像的骨架圖像(各骨架圖像如圖3的右邊圖像所示),該處理單元3時序地處理該等骨架圖像得到各骨架圖像中的骨架的各節點的位移改變量, 同時將各節點的位移改變量經由該判斷程式31進行該受測者8的跌倒動作辨識。In the processing of the fall detection, the processing unit 3 performs skeleton recognition processing on the subject 8 in the depth images to obtain a plurality of skeleton images respectively corresponding to the depth images (each skeleton map) As shown in the right image of FIG. 3, the processing unit 3 sequentially processes the skeleton images to obtain the displacement change amount of each node of the skeleton in each skeleton image, At the same time, the displacement change amount of each node is subjected to the fall detection operation of the subject 8 via the determination program 31.
以上所述的該受測者8的位置辨識及骨架辨識處理都是經由kinect系統的軟體運算處理。The position recognition and skeleton recognition processing of the subject 8 described above are all processed by the software of the kinect system.
然而該受測者8的跌倒動作的判斷依據,是與事先連續紀錄該受測者8的日常居家動作所造成的骨架節點的位移變化量做比對,其中,主要是對於與跌倒相似的坐下、彎腰及蹲下等動作做分析,並於該判斷程式31建立一跌倒動作判斷式。由於骨架中的頭部節點的位移變化量是最大,所以以骨架中的頭部節點位移變化量為例子,如圖4所示,該受測者8跌倒時的頭部節點的位移量是明顯不同於在彎腰及蹲下時的頭部節點的位移變化量。However, the judgment of the fall action of the subject 8 is based on the comparison of the displacement change of the skeleton node caused by the continuous daily recording of the subject's daily home movement, wherein the main purpose is to sit similar to the fall. The lower, the bending and the underarm are analyzed, and a fall action judgment is established in the judgment program 31. Since the amount of displacement of the head node in the skeleton is the largest, the displacement amount of the head node in the skeleton is taken as an example. As shown in FIG. 4, the displacement of the head node when the subject 8 falls is obvious. It is different from the amount of displacement of the head node when bending and kneeling.
參閱圖2,該通報單元4電連接該處理單元3,當該處理單元3辨識出該受測者8已跌倒時,控制該通報單元4向外發出該跌倒信息。在本較佳實施例中,該通報單元4是會發出警報聲向外通知該空間9外的其他人,且對該受測者8的親友發出簡訊告知該受測者8已跌倒,以在最短的時間做出跌倒事件的處理。Referring to FIG. 2, the notification unit 4 is electrically connected to the processing unit 3. When the processing unit 3 recognizes that the subject 8 has fallen, the notification unit 4 is controlled to issue the fall information. In the preferred embodiment, the notification unit 4 sends an alarm sound to notify other people outside the space 9, and sends a message to the relatives and friends of the subject 8 to inform the subject 8 that the user has fallen. The shortest time to deal with the fall event.
綜上所述,本新型具自動追蹤之跌倒偵測系統藉由該深度感測單元2取得多數的深度圖像且經由該處理單元3的影像處理後,進行該受測者8的自動追蹤控制及該受測者8的跌倒動作辨識,以達到可自動追蹤該受測者8及該受測者跌倒動作偵測的功效。以上跌倒動作偵測方式不僅讓受測者8不需配戴任何感測器而方便使用,且該深 度感測單元2截取的圖像是深度圖像,沒有一般傳統攝影機清楚記錄該受測者的外貌隱私問題,除此之外,該通報單元4在該處理單元2辨識出該受測者8發生跌倒動作時,可及時向外發出跌倒信息,以通知他人做出及時的跌倒事件處理,故確實能達成本新型之目的。In summary, the fall detection system with automatic tracking of the present invention obtains a plurality of depth images by the depth sensing unit 2 and performs automatic tracking control of the subject 8 after the image processing by the processing unit 3 And the fall of the subject 8 is identified to achieve automatic tracking of the subject 8 and the subject's fall detection. The above fall detection method not only makes the subject 8 easy to use without wearing any sensor, and the depth The image captured by the degree sensing unit 2 is a depth image, and the conventional camera does not clearly record the appearance privacy problem of the subject. In addition, the notification unit 4 recognizes the subject 8 in the processing unit 2 When a fall action occurs, the fall information can be sent out in time to inform others to make a timely fall event, so it is indeed possible to achieve the purpose of the novel.
惟以上所述者,僅為本新型之較佳實施例而已,當不能以此限定本新型實施之範圍,即大凡依本新型申請專利範圍及專利說明書內容所作之簡單的等效變化與修飾,皆仍屬本新型專利涵蓋之範圍內。However, the above is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, that is, the simple equivalent changes and modifications made in accordance with the scope of the present patent application and the contents of the patent specification, All remain within the scope of this new patent.
1‧‧‧旋轉載台1‧‧‧Rotary stage
2‧‧‧深度感測單元2‧‧‧Deep sensing unit
21‧‧‧紅外線發射器21‧‧‧Infrared emitter
22‧‧‧紅外線攝影機22‧‧‧Infrared camera
8‧‧‧受測者8‧‧‧ Subjects
9‧‧‧空間9‧‧‧ Space
Claims (3)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW102211175U TWM463402U (en) | 2013-06-14 | 2013-06-14 | Falling down detection system with automatic tracking |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW102211175U TWM463402U (en) | 2013-06-14 | 2013-06-14 | Falling down detection system with automatic tracking |
Publications (1)
Publication Number | Publication Date |
---|---|
TWM463402U true TWM463402U (en) | 2013-10-11 |
Family
ID=49772526
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW102211175U TWM463402U (en) | 2013-06-14 | 2013-06-14 | Falling down detection system with automatic tracking |
Country Status (1)
Country | Link |
---|---|
TW (1) | TWM463402U (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI620152B (en) * | 2016-10-19 | 2018-04-01 | Fall detection method | |
CN113796853A (en) * | 2020-06-16 | 2021-12-17 | 广州印芯半导体技术有限公司 | Optical image comparison system and comparison method thereof |
-
2013
- 2013-06-14 TW TW102211175U patent/TWM463402U/en not_active IP Right Cessation
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TWI620152B (en) * | 2016-10-19 | 2018-04-01 | Fall detection method | |
CN113796853A (en) * | 2020-06-16 | 2021-12-17 | 广州印芯半导体技术有限公司 | Optical image comparison system and comparison method thereof |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20210183516A1 (en) | Systems and methods to identify persons and/or identify and quantify pain, fatigue, mood, and intent with protection of privacy | |
Stone et al. | Fall detection in homes of older adults using the Microsoft Kinect | |
Erden et al. | Sensors in assisted living: A survey of signal and image processing methods | |
US11276181B2 (en) | Systems and methods for use in detecting falls utilizing thermal sensing | |
Huang et al. | Multimodal sleeping posture classification | |
Parate et al. | Risq: Recognizing smoking gestures with inertial sensors on a wristband | |
CN108670261B (en) | Motion state detection method, wearable device and apparatus | |
Bevilacqua et al. | Fall detection in indoor environment with kinect sensor | |
JP6915421B2 (en) | Watching support system and its control method | |
CN108335458A (en) | It is a kind of to see that the domestic intelligent of people sees guard system and its keeps an eye on method | |
JP7375770B2 (en) | Information processing device, information processing method, and program | |
JP2016067641A (en) | Fall detection processing apparatus and fall detection system | |
JP3378540B2 (en) | Abnormality determination device and program recording medium | |
WO2020145380A1 (en) | Care recording device, care recording system, care recording program, and care recording method | |
TWI541769B (en) | Falling down detecting systems and method | |
TWM463402U (en) | Falling down detection system with automatic tracking | |
TWI474291B (en) | Somatosensory fall-detection method | |
Adolf et al. | Deep neural network based body posture recognitions and fall detection from low resolution infrared array sensor | |
WO2021093543A1 (en) | Scanner and operation method, apparatus and system therefor, and storage medium and processor | |
JP3767898B2 (en) | Human behavior understanding system | |
Bansal et al. | Elderly people fall detection system using skeleton tracking and recognition | |
Ianculescu et al. | Improving the Elderly’s Fall Management through Innovative Personalized Remote Monitoring Solution | |
JP7437919B2 (en) | Judgment system and judgment program | |
US20110288377A1 (en) | Biological information measurement apparatus and method thereof | |
JP6726256B2 (en) | Program, game server, information processing terminal, method and game system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
MM4K | Annulment or lapse of a utility model due to non-payment of fees |